Radar Signal and Data Processing with Applications

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400
Eng eng	-	2.1	2020	21.5 Days	CHF 1200
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700
Signals signals	-	3.2	2020	28.3 Days	CHF 1000
Telecom telecom	2.1	4.8	2020	20.5 Days	CHF 1200

20 pages, 58910 KiB

Open AccessArticle

A 3D Blur Suppression Method for High-Resolution and Wide-Swath Blurred Images Based on Estimating and Eliminating Defocused Point Clouds

by Yuling Liu, Fubo Zhang, Longyong Chen and Tao Jiang

Remote Sens. 2025, 17(5), 928; https://doi.org/10.3390/rs17050928 - 5 Mar 2025

Viewed by 412

Abstract

Traditional single-channel Synthetic Aperture Radar (SAR) cannot achieve high-resolution and wide-swath (HRWS) imaging due to the constraint of the minimum antenna area. Distributed HRWS SAR can realize HRWS imaging and also possesses the resolution ability in the height dimension by arranging multiple satellites [...] Read more.

Traditional single-channel Synthetic Aperture Radar (SAR) cannot achieve high-resolution and wide-swath (HRWS) imaging due to the constraint of the minimum antenna area. Distributed HRWS SAR can realize HRWS imaging and also possesses the resolution ability in the height dimension by arranging multiple satellites in the elevation direction. Nevertheless, due to the excessively high pulse repetition frequency (PRF) of the distributed SAR system, range ambiguity will occur in large detection scenarios. When directly performing 3D-imaging processing on SAR images with range ambiguity, both focused point clouds and blurred point clouds will exist simultaneously in the generated 3D point clouds, which affects the quality of the generated 3D-imaging point clouds. To address this problem, this paper proposes a 3D blur suppression method for HRWS blurred images, which estimates and eliminates defocused point clouds based on focused targets. The echoes with range ambiguity are focused in the near area and the far area, respectively. Then, through image registration, amplitude and phase correction, and height-direction focusing, the point clouds in the near area and the far area are obtained. The strongest points in the two sets of point clouds are iteratively selected to estimate and eliminate the defocused point clouds in the other set of point clouds until all the ambiguity is eliminated. Simulation experiments based on airborne measured data verified the capability to achieve HRWS 3D blur suppression of this method. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 885 KiB

Open AccessArticle

An Optimization Method for Multi-Functional Radar Network Deployment in Complex Regions

by Yi Han, Xueting Li, Xiangliang Xu, Zhenxing Zhang, Tianxian Zhang and Xiaobo Yang

Remote Sens. 2025, 17(4), 730; https://doi.org/10.3390/rs17040730 - 19 Feb 2025

Cited by 1 | Viewed by 466

Abstract

This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the proposed methodology leverages the unique characteristics [...] Read more.

This paper addresses the deployment of a multi-functional radar network (MFRN) in complex regions that may exhibit non-connectivity, holes, or concave shapes, utilizing multi-objective particle swarm optimization (MOPSO). Unlike traditional approaches that rely on constraint-handling techniques, the proposed methodology leverages the unique characteristics of polygonal deployment regions to enhance deployment efficiency. Specifically, for the aforementioned complex deployment regions, a region decomposition approach based on convex partitioning is proposed. This approach allows for the decomposition of complex regions into multiple non-overlapping convex subregions. Moreover, for convex deployment regions or subregions, we propose a coordinate transformation approach to eliminate the constraints introduced by the shape of the convex region. By combining the above approaches, we introduce a novel MOPSO based on decomposition and transformation, named MOPSO-DT. This algorithm aims to optimize MFRN deployment in these challenging environments. Experimental results demonstrate the superiority of the MOPSO-DT algorithm over two existing algorithms across a variety of deployment cases, highlighting its enhanced efficiency, effectiveness, and stability. These findings indicate that the proposed algorithm is well suited for optimizing MFRN deployment in complex, irregular regions, offering significant improvements in performance compared to conventional methods. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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22 pages, 2943 KiB

Open AccessArticle

Characterization of 77 GHz Radar Backscattering from Sea Surfaces at Low Incidence Angles: Preliminary Results

by Qinghui Xu, Chen Zhao, Zezong Chen, Sitao Wu, Xiao Wang and Lingang Fan

Remote Sens. 2025, 17(1), 116; https://doi.org/10.3390/rs17010116 - 1 Jan 2025

Viewed by 777

Abstract

Millimeter-wave (MMW) radar is capable of providing high temporal–spatial measurements of the ocean surface. Some topics, such as the characterization of the radar echo, have attracted widespread attention from researchers. However, most existing research studies focus on the backscatter of the ocean surface [...] Read more.

Millimeter-wave (MMW) radar is capable of providing high temporal–spatial measurements of the ocean surface. Some topics, such as the characterization of the radar echo, have attracted widespread attention from researchers. However, most existing research studies focus on the backscatter of the ocean surface at low microwave bands, while the sea surface backscattering mechanism in the 77 GHz frequency band remains not well interpreted. To address this issue, in this paper, the investigation of the scattering mechanism is carried out for the 77 GHz frequency band ocean surface at small incidence angles. The backscattering coefficient is first simulated by applying the quasi-specular scattering model and the corrected scattering model of geometric optics (GO4), using two different ocean wave spectrum models (the Hwang spectrum and the Kudryavtsev spectrum). Then, the dependence of the sea surface normalized radar cross section (NRCS) on incidence angles, azimuth angles, and sea states are investigated. Finally, by comparison between model simulations and the radar-measured data, the 77 GHz frequency band scattering characterization of sea surfaces at the near-nadir incidence is verified. In addition, experimental results from the wave tank are shown, and the difference in the scattering mechanism is further discussed between water surfaces and oceans. The obtained results seem promising for a better understanding of the ocean surface backscattering mechanism in the MMW frequency band. It provides a new method for fostering the usage of radar technologies for real-time ocean observations. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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26 pages, 5419 KiB

Open AccessArticle

Observability-Based Gaussian Sum Cubature Kalman Filter for Three-Dimensional Target Tracking Using a Single Two-Dimensional Radar

by Haonan Jiang, Yingjie Zhang, Xiaotong Wang and Yuanli Cai

Remote Sens. 2024, 16(22), 4172; https://doi.org/10.3390/rs16224172 - 8 Nov 2024

Cited by 1 | Viewed by 940

Abstract

This paper considers the problem of tracking a three-dimensional target under the condition that only a single two-dimensional radar is available. Since a two-dimensional radar can only measure the slant range and azimuth information relative to the target, an unobservability issue arises in [...] Read more.

This paper considers the problem of tracking a three-dimensional target under the condition that only a single two-dimensional radar is available. Since a two-dimensional radar can only measure the slant range and azimuth information relative to the target, an unobservability issue arises in this tracking application. Therefore, we first investigate the observability issue of tracking a three-dimensional target with a single two-dimensional radar from two perspectives, including intuitive illustration and quantitative analysis. From the perspective of intuitive illustration, we demonstrate “What is the unobservability issue” and “How does the relative target-radar geometry influence the observability of the tracking system”. From the perspective of quantitative analysis, we construct a novel observability metric for this special tracking problem. Second, aiming at improving tracking performance under the unobservability of target height, we propose an observability-based Gaussian sum cubature Kalman filter. Built within the Gaussian sum framework and based on the height-parameterized strategy, this novel algorithm uses a set of independent fifth-degree cubature Kalman filters, each of which can detect the system observability variation and enhance the tracking accuracy by using a Gaussian splitting scheme under low-degree observability. Finally, the effectiveness of the presented filtering algorithm is validated through lots of simulation experiments. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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33 pages, 14046 KiB

Open AccessArticle

High-Resolution Collaborative Forward-Looking Imaging Using Distributed MIMO Arrays

by Shipei Shen, Xiaoli Niu, Jundong Guo, Zhaohui Zhang and Song Han

Remote Sens. 2024, 16(21), 3991; https://doi.org/10.3390/rs16213991 - 27 Oct 2024

Viewed by 1603

Abstract

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where [...] Read more.

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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22 pages, 11121 KiB

Open AccessArticle

Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning

by Longshuai Wang, Liwen Ma, Tao Wu, Jiaji Wu and Xiang Luo

Remote Sens. 2024, 16(20), 3891; https://doi.org/10.3390/rs16203891 - 19 Oct 2024

Cited by 2 | Viewed by 1388

Abstract

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type [...] Read more.

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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30 pages, 4085 KiB

Open AccessArticle

A Highly Efficient Compressive Sensing Algorithm Based on Root-Sparse Bayesian Learning for RFPA Radar

by Ju Wang, Bingqi Shan, Song Duan and Qin Zhang

Remote Sens. 2024, 16(19), 3564; https://doi.org/10.3390/rs16193564 - 25 Sep 2024

Viewed by 817

Abstract

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL [...] Read more.

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during the fine search stage. Meanwhile, to ensure computational feasibility, the proposed algorithm utilizes a simple single-level hierarchical prior distribution model and employs the derived root-solving formula to avoid the refinement of velocity grids. Moreover, during the fine search stage, the proposed algorithm combines the fixed-point strategy with the Expectation-Maximization algorithm to update the hyperparameters, further reducing computational complexity. In terms of implementation, the proposed algorithm updates hyperparameters based on the single-level prior distribution to approximate values for the range and velocity parameters during the coarse search stage. Subsequently, in the fine search stage, the proposed algorithm performs a grid search only in the range dimension and uses the derived root-solving formula to directly solve for the target velocity parameters. Simulation results demonstrate that the proposed algorithm maintains low computational complexity while exhibiting stable performance for parameter estimation in various multi-target off-grid scenarios. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 4710 KiB

Open AccessArticle

TWPT: Through-Wall Position Detection and Tracking System Using IR-UWB Radar Utilizing Kalman Filter-Based Clutter Reduction and CLEAN Algorithm

by Jinlong Zhang, Xiaochao Dang and Zhanjun Hao

Electronics 2024, 13(19), 3792; https://doi.org/10.3390/electronics13193792 - 24 Sep 2024

Viewed by 1242

Abstract

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve [...] Read more.

Against the backdrop of rapidly advancing Artificial Intelligence of Things (AIOT) and sensing technologies, there is a growing demand for indoor location-based services (LBSs). This paper proposes a through-the-wall localization and tracking (TWPT) system based on an improved ultra-wideband (IR-UWB) radar to achieve more accurate localization of indoor moving targets. The TWPT system overcomes the limitations of traditional localization methods, such as multipath effects and environmental interference, using the high penetration and high accuracy of IR-UWB radar based on multi-sensor fusion technology. In the study, an improved Kalman filter (KF) algorithm is used for clutter reduction, while the CLEAN algorithm, combined with a compensation mechanism, is utilized to increase the target detection probability. Finally, a three-point localization estimation algorithm based on multi-IR-UWB radar is applied for the precise position and trajectory estimation of the target. Experimental validation indicates the TWPT system achieves a high positioning accuracy of 96.91%, with a root mean square error (RMSE) of 0.082 m and a Maximum Position Error (MPE) of 0.259 m. This study provides a highly accurate and precise solution for indoor TWPT based on IR-UWB radar. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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29 pages, 8205 KiB

Open AccessArticle

A Robust Translational Motion Compensation Method for Moving Target ISAR Imaging Based on Phase Difference-Lv’s Distribution and Auto-Cross-Correlation Algorithm

by Can Liu, Yunhua Luo and Zhongjun Yu

Remote Sens. 2024, 16(19), 3554; https://doi.org/10.3390/rs16193554 - 24 Sep 2024

Cited by 1 | Viewed by 1228

Abstract

Translational motion compensation constitutes a pivotal and essential procedure in inverse synthetic aperture radar (ISAR) imaging. Many researchers have previously proposed their methods to address this requirement. However, conventional methods may struggle to produce satisfactory results when dealing with non-stationary moving targets or [...] Read more.

Translational motion compensation constitutes a pivotal and essential procedure in inverse synthetic aperture radar (ISAR) imaging. Many researchers have previously proposed their methods to address this requirement. However, conventional methods may struggle to produce satisfactory results when dealing with non-stationary moving targets or operating under conditions of low signal-to-noise ratios (SNR). Aiming at this challenge, this article proposes a parametric non-search method that contains two main stages. The radar echoes can be modeled as polynomial phase signals (PPS). In the initial stage, the energy of the received two-dimensional signal is coherently integrated into a peak point by leveraging phase difference (PD) and Lv’s distribution (LVD), from which the high-order polynomial coefficients can be obtained accurately. The estimation of the first-order coefficients is conducted during the second stage. The auto-cross-correlation function for range profiles is introduced to enhance the accuracy and robustness of estimation. Subsequently, a novel mathematical model for velocity estimation is proposed, and its least squares solution is derived. Through this model, a sub-resolution solution can be obtained without requiring interpolation. By employing all the estimated polynomial coefficients, the non-stationary motion of the target can be fully compensated, yielding the acquisition of a finely focused image. Finally, the experimental findings validate the superiority and robustness of the proposed method in comparison to state-of-the-art approaches. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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27 pages, 2238 KiB

Open AccessArticle

Contrastive Transformer Network for Track Segment Association with Two-Stage Online Method

by Zongqing Cao, Bing Liu, Jianchao Yang, Ke Tan, Zheng Dai, Xingyu Lu and Hong Gu

Remote Sens. 2024, 16(18), 3380; https://doi.org/10.3390/rs16183380 - 11 Sep 2024

Viewed by 976

Abstract

Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility [...] Read more.

Interrupted and multi-source track segment association (TSA) are two key challenges in target trajectory research within radar data processing. Traditional methods often rely on simplistic assumptions about target motion and statistical techniques for track association, leading to problems such as unrealistic assumptions, susceptibility to noise, and suboptimal performance limits. This study proposes a unified framework to address the challenges of associating interrupted and multi-source track segments by measuring trajectory similarity. We present TSA-cTFER, a novel network utilizing contrastive learning and TransFormer Encoder to accurately assess trajectory similarity through learned Representations by computing distances between high-dimensional feature vectors. Additionally, we tackle dynamic association scenarios with a two-stage online algorithm designed to manage tracks that appear or disappear at any time. This algorithm categorizes track pairs into easy and hard groups, employing tailored association strategies to achieve precise and robust associations in dynamic environments. Experimental results on real-world datasets demonstrate that our proposed TSA-cTFER network with the two-stage online algorithm outperforms existing methods, achieving 94.59% accuracy in interrupted track segment association tasks and 94.83% in multi-source track segment association tasks. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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22 pages, 9013 KiB

Open AccessArticle

Application of Instance Segmentation to Identifying Insect Concentrations in Data from an Entomological Radar

by Rui Wang, Jiahao Ren, Weidong Li, Teng Yu, Fan Zhang and Jiangtao Wang

Remote Sens. 2024, 16(17), 3330; https://doi.org/10.3390/rs16173330 - 8 Sep 2024

Cited by 2 | Viewed by 1365

Abstract

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation [...] Read more.

Entomological radar is one of the most effective tools for monitoring insect migration, capable of detecting migratory insects concentrated in layers and facilitating the analysis of insect migration behavior. However, traditional entomological radar, with its low resolution, can only provide a rough observation of layer concentrations. The advent of High-Resolution Phased Array Radar (HPAR) has transformed this situation. With its high range resolution and high data update rate, HPAR can generate detailed concentration spatiotemporal distribution heatmaps. This technology facilitates the detection of changes in insect concentrations across different time periods and altitudes, thereby enabling the observation of large-scale take-off, landing, and layering phenomena. However, the lack of effective techniques for extracting insect concentration data of different phenomena from these heatmaps significantly limits detailed analyses of insect migration patterns. This paper is the first to apply instance segmentation technology to the extraction of insect data, proposing a method for segmenting and extracting insect concentration data from spatiotemporal distribution heatmaps at different phenomena. To address the characteristics of concentrations in spatiotemporal distributions, we developed the Heatmap Feature Fusion Network (HFF-Net). In HFF-Net, we incorporate the Global Context (GC) module to enhance feature extraction of concentration distributions, utilize the Atrous Spatial Pyramid Pooling with Depthwise Separable Convolution (SASPP) module to extend the receptive field for understanding various spatiotemporal distributions of concentrations, and refine segmentation masks with the Deformable Convolution Mask Fusion (DCMF) module to enhance segmentation detail. Experimental results show that our proposed network can effectively segment concentrations of different phenomena from heatmaps, providing technical support for detailed and systematic studies of insect migration behavior. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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20 pages, 972 KiB

Open AccessArticle

Radar Anti-Jamming Performance Evaluation Based on Logistic Fusion of Multi-Stage SIR Information

by Linqi Zhao, Liang Yan, Xiaojun Duan and Zhengming Wang

Remote Sens. 2024, 16(17), 3214; https://doi.org/10.3390/rs16173214 - 30 Aug 2024

Viewed by 810

Abstract

When assessing radar anti-jamming performance, the challenge of limited sample sizes is a significant hurdle. In response, this paper introduces a logistic fusion model that leverages Bayesian techniques and a Monte Carlo Markov chain (MCMC) sampling method based on a logistic regression model [...] Read more.

When assessing radar anti-jamming performance, the challenge of limited sample sizes is a significant hurdle. In response, this paper introduces a logistic fusion model that leverages Bayesian techniques and a Monte Carlo Markov chain (MCMC) sampling method based on a logistic regression model that characterizes the relationship between the signal-to-interference ratio (SIR) and the anti-jamming rate. The logistic curve’s inflection point and growth rate serve as crucial indices for evaluating radar anti-jamming performance, providing insights into the SIR threshold for successful jamming mitigation. The proposed model allows for the derivation of posterior distributions for these parameters using the MCMC sampling method and kernel density estimation. It also enables the fusion of anti-jamming data from multiple stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Through extensive simulations, our method achieves a remarkably low root mean square error (RMSE) of

0.0552

. Compared with a conventional BETA fusion model, our proposed logistic fusion approach demonstrates superior performance and robustness in accurately estimating the anti-jamming rate. The fusion of multi-stage data, even with varying levels of reliability, improves the overall accuracy of the performance evaluation. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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25 pages, 4149 KiB

Open AccessArticle

A Multi-Objective Intelligent Optimization Method for Sensor Array Optimization in Distributed SAR-GMTI Radar Systems

by Xianghai Li, Rong Wang, Gengchen Liang and Zhiwei Yang

Remote Sens. 2024, 16(16), 3041; https://doi.org/10.3390/rs16163041 - 19 Aug 2024

Cited by 3 | Viewed by 1069

Abstract

The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to [...] Read more.

The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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22 pages, 1872 KiB

Open AccessArticle

Sensing-Efficient Transmit Beamforming for ISAC with MIMO Radar and MU-MIMO Communication

by Huimin Liu, Yong Li, Wei Cheng, Limeng Dong and Beiming Yan

Remote Sens. 2024, 16(16), 3028; https://doi.org/10.3390/rs16163028 - 18 Aug 2024

Cited by 3 | Viewed by 1795

Abstract

We focus on an integrated sensing and communication (ISAC) system—a single platform equipped with multiple antennas transmitting a waveform to detect targets and communicate with downlink users. Due to spectrum sharing between multiple-input–multiple-output (MIMO) radar and multiuser MIMO (MU-MIMO) communication, beamforming is becoming [...] Read more.

We focus on an integrated sensing and communication (ISAC) system—a single platform equipped with multiple antennas transmitting a waveform to detect targets and communicate with downlink users. Due to spectrum sharing between multiple-input–multiple-output (MIMO) radar and multiuser MIMO (MU-MIMO) communication, beamforming is becoming increasingly important as a technique that enables the creation of directional beams. In this paper, we propose a novel joint transmit beamforming design scheme that employs a beam pattern approximation strategy for radar sensing and utilizes rate-splitting for multiuser communication offering advanced interference management strategies. The optimization problems are formulated from both radar-centric and trade-off viewpoints. First, we propose a radar-centric beamforming scheme to achieve sensing efficiency through beam pattern approximation, while requiring the fairness signal-to-interference-plus-noise ratio (SINR) to be higher than a given threshold to guarantee a minimal level of communication quality, while the obtained performance for the communication system is limited in this scheme. To address this problem, we propose a beamforming design scheme from a trade-off viewpoint that flexibly optimizes both sensing and communication performances with a regularization parameter. Finally, we propose a partial rate-splitting-based beamforming design method aimed at maximizing the effective sensing power, with the constraint of a minimal sum rate for downlink users. Numerical results are provided to assess the effectiveness of all proposed schemes. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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12 pages, 4351 KiB

Open AccessCommunication

Automatic Estimation of Tropical Cyclone Centers from Wide-Swath Synthetic-Aperture Radar Images of Miniaturized Satellites

by Yan Wang, Haihua Fu, Lizhen Hu, Xupu Geng, Shaoping Shang, Zhigang He, Yanshuang Xie and Guomei Wei

Appl. Sci. 2024, 14(16), 7047; https://doi.org/10.3390/app14167047 - 11 Aug 2024

Viewed by 1442

Abstract

Synthetic-Aperture Radar (SAR) has emerged as an important tool for monitoring tropical cyclones (TCs) due to its high spatial resolution and cloud-penetrating capability. Recent advancements in SAR technology have led to smaller and lighter satellites, yet few studies have evaluated their effectiveness in [...] Read more.

Synthetic-Aperture Radar (SAR) has emerged as an important tool for monitoring tropical cyclones (TCs) due to its high spatial resolution and cloud-penetrating capability. Recent advancements in SAR technology have led to smaller and lighter satellites, yet few studies have evaluated their effectiveness in TC monitoring. This paper employs an algorithm for automatic TC center location, involving three stages: coarse estimation from a whole SAR image; precise estimation from a sub-SAR image; and final identification of the center using the lowest Normalized Radar Cross-Section (NRCS) value within a smaller sub-SAR image. Using three wide-swath miniaturized SAR images of TC Noru (2022), and TCs Doksuri and Koinu (2023), the algorithm’s accuracy was validated by comparing estimated TC center positions with visually located data. For TC Noru, the distances for the three stages were 21.42 km, 14.39 km, and 8.19 km; for TC Doksuri—14.36 km, 20.48 km, and 17.10 km; and for TC Koinu—47.82 km, 31.59 km, and 5.42 km. The results demonstrate the potential of miniaturized SAR in TC monitoring. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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19 pages, 48324 KiB

Open AccessArticle

An Efficient and Accurate Ground-Based Synthetic Aperture Radar (GB-SAR) Real-Time Imaging Scheme Based on Parallel Processing Mode and Architecture

by Yunxin Tan, Guangju Li, Chun Zhang and Weiming Gan

Electronics 2024, 13(16), 3138; https://doi.org/10.3390/electronics13163138 - 8 Aug 2024

Viewed by 1532

Abstract

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been [...] Read more.

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional

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K imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the

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processing model, and the

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processing model. The

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processing model utilizes

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, and

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for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm’s performance in dynamic parallelism and alleviating the issue of serial execution within the

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processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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18 pages, 1320 KiB

Open AccessArticle

Polarimetric Adaptive Coherent Detection in Lognorm-Texture-Distributed Sea Clutter

by Jian Xue, Jiali Yan, Shuwen Xu and Jun Liu

Remote Sens. 2024, 16(15), 2841; https://doi.org/10.3390/rs16152841 - 2 Aug 2024

Viewed by 917

Abstract

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric [...] Read more.

This paper addresses polarimetric adaptive coherent detection of radar targets embedded in sea clutter. Initially, radar clutter data across multiple polarimetric channels is modeled using a compound Gaussian framework featuring an unspecified speckle covariance matrix and lognormal texture distribution. Subsequently, three adaptive polarimetric coherent detectors are derived, employing parameter estimation and two-step versions of the generalized likelihood ratio test (GLRT): the complex parameter Rao and Wald tests. These detectors utilize both clutter texture distribution information and radar data’s polarimetric aspects to enhance detection performance. Simulation experiments demonstrate the superiority of three proposed detectors over the competitors, and that they are sensitive to polarimetric channel parameters such as secondary data quantity, target or clutter speckle correlation, and signal-to-clutter ratio disparity. Additionally, the proposed detectors exhibit a near-constant false alarm rate relative to average clutter power and speckle covariance matrix. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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23 pages, 17859 KiB

Open AccessArticle

Attention Mechanism and Neural Ordinary Differential Equations for the Incomplete Trajectory Information Prediction of Unmanned Aerial Vehicles Using Airborne Radar

by Haojie Peng, Wei Yang, Zheng Wang and Ruihai Chen

Electronics 2024, 13(15), 2938; https://doi.org/10.3390/electronics13152938 - 25 Jul 2024

Viewed by 1415

Abstract

Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed method exhibits high accuracy [...] Read more.

Due to the potential for airborne radar to capture incomplete observational information regarding unmanned aerial vehicle (UAV) trajectories, this study introduces a novel approach called Node-former, which integrates neural ordinary differential equations (NODEs) and the Informer framework. The proposed method exhibits high accuracy in trajectory prediction, even in scenarios with prolonged data interruptions. Initially, data outside the acceptable error range are discarded to mitigate the impact of interruptions on prediction accuracy. Subsequently, to address the irregular sampling caused by data elimination, NODEs are utilized to transform computational interpolation into an initial value problem (IPV), thus preserving informative features. Furthermore, this study enhances the Informer’s encoder through the utilization of time-series prior knowledge and introduces an ODE solver as the decoder to mitigate fluctuations in the original decoder’s output. This approach not only accelerates feature extraction for long sequence data, but also ensures smooth and robust output values. Experimental results demonstrate the superior performance of Node-former in trajectory prediction with interrupted data compared to traditional algorithms. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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18 pages, 24932 KiB

Open AccessArticle

A Waveform Design for Integrated Radar and Jamming Based on Smart Modulation and Complementary Coding

by Huabin Yan, Shiyuan Zhang, Xingyu Lu, Jianchao Yang, Lunhao Duan, Ke Tan and Hong Gu

Remote Sens. 2024, 16(15), 2725; https://doi.org/10.3390/rs16152725 - 25 Jul 2024

Cited by 2 | Viewed by 1693

Abstract

Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have shown limited design flexibility, high levels of sidelobe in detection results, and overall [...] Read more.

Waveform design for integrated radar and jamming is generally based on the concept of shared waveform, which uses jamming signals without typical radar signal characteristics for detection. Existing waveforms have shown limited design flexibility, high levels of sidelobe in detection results, and overall ordinary performance. We propose an integrated radar and jamming waveform based on smart modulation and complementary coding. Unlike traditional integrated radar and jamming waveform based on smart modulation, the phase angle of the binary phase-coded sequence is adjustable in this smart modulation method, allowing for a controllable jamming effect, achieving true smart modulation. However, this smart modulation waveform also suffers from high sidelobes in detection. To address this issue, we take a complementary coding approach and design a smart modulation waveform with complementary characteristics. This waveform can synthesize a complete linear frequency modulation (LFM) signal by adding two pulses together, thereby reducing the sidelobes in the smart modulation waveform and enhancing its detection performance. Theoretical analysis indicates that the detection and jamming effects of this integrated waveform can be flexibly controlled by adjusting the phase angles of the coding sequences. Simulation analysis and experimental results confirm the significant advantages of this waveform. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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34 pages, 756 KiB

Open AccessArticle

Dynamic Programming-Based Track-before-Detect Algorithm for Weak Maneuvering Targets in Range–Doppler Plane

by Xinghui Wu, Jieru Ding, Zhiyi Wang and Min Wang

Remote Sens. 2024, 16(14), 2639; https://doi.org/10.3390/rs16142639 - 18 Jul 2024

Viewed by 1254

Abstract

This paper focuses on detecting and tracking maneuvering weak targets in the range–Doppler (RD) plane with the track-before-detect (TBD) algorithm based on dynamic programming (DP). Traditional DP-TBD algorithms integrate target detection and tracking in their framework while searching the paths provided by a [...] Read more.

This paper focuses on detecting and tracking maneuvering weak targets in the range–Doppler (RD) plane with the track-before-detect (TBD) algorithm based on dynamic programming (DP). Traditional DP-TBD algorithms integrate target detection and tracking in their framework while searching the paths provided by a predefined model of the kinematic properties within the constraints allowed. However, both the approximate motion model used in the RD plane and the model mismatch caused when the target undergoes a maneuver can degrade the TBD performance sharply. To address these issues, this paper accurately describes the evolution of the RD equation based on Constant Acceleration (CA) and Coordinated Turn (CT) motion models with the process noise in the Cartesian coordinate system, and it also employs a recursive method to estimate the parameters in the equations for efficient energy accumulation and path searches. Facing the situation that targets energy accumulation during the DP iteration process will lead to an expansion of the target energy accumulation process. This paper designs a more efficient Optimization Function (OF) to inhibit the expansion effect, improve the resolution of the nearby targets, and increase the detection probability of the weak targets simultaneously. In addition, to search the trajectory more efficiently and accurately, we improved the process of DP multi-frame accumulation, thus reducing the computation amount of large-scale searches. Finally, the effectiveness of the proposed method for CA and CT motion target detection and tracking is verified by many of the simulation experiments that were conducted in this paper. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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16 pages, 2211 KiB

Open AccessArticle

Modulus Waveform Design Based on Manifold ADMM Idea in Dual-Function Radar–Communication System

by Yinan Zhao, Zhongqing Zhao, Fangqiu Tong, Yu Fan and Xiang Feng

Electronics 2024, 13(14), 2726; https://doi.org/10.3390/electronics13142726 - 11 Jul 2024

Viewed by 1147

Abstract

In this paper, we try to design the joint waveform and passive beamforming within the context of dual-function radar–communication (DFRC) systems. Focusing on the intricate trade-off between stringent radar beampattern constraints and their desired performance, we introduce a novel manifold idea based on [...] Read more.

In this paper, we try to design the joint waveform and passive beamforming within the context of dual-function radar–communication (DFRC) systems. Focusing on the intricate trade-off between stringent radar beampattern constraints and their desired performance, we introduce a novel manifold idea based on the alternating direction method of multipliers (ADMM) framework. Specifically, our proposed method, named DFRC-MA, could address the challenge of constant modulus waveform design in a multiple-input–multiple-output (MIMO) DFRC system. Firstly, our methodology begins by formulating the reference waveform to achieve an optimal radar beamforming pattern. Subsequently, we define the DFRC optimization problem to mitigate the multi-user interference (MUI) under the constant modulus constraint. Through a series of simulations, we evaluate the efficacy of DFRC-MA, where the integrated waveform designed by DFRC-MA exhibits superior performance over some prevalent ones. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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22 pages, 8686 KiB

Open AccessArticle

Weakly Supervised Transformer for Radar Jamming Recognition

by Menglu Zhang, Yushi Chen and Ye Zhang

Remote Sens. 2024, 16(14), 2541; https://doi.org/10.3390/rs16142541 - 10 Jul 2024

Cited by 1 | Viewed by 1298

Abstract

Radar jamming recognition is a key step in electronic countermeasures, and accurate and sufficient labeled samples are essential for supervised learning-based recognition methods. However, in real practice, collected radar jamming samples often have weak labels (i.e., noisy-labeled or unlabeled ones), which degrade recognition [...] Read more.

Radar jamming recognition is a key step in electronic countermeasures, and accurate and sufficient labeled samples are essential for supervised learning-based recognition methods. However, in real practice, collected radar jamming samples often have weak labels (i.e., noisy-labeled or unlabeled ones), which degrade recognition performance. Additionally, recognition performance is hindered by limitations in capturing the global features of radar jamming. The Transformer (TR) has advantages in modeling long-range relationships. Therefore, a weakly supervised Transformer is proposed to address the issues of performance degradation under weak supervision. Specifically, complementary label (CL) TR, called RadarCL-TR, is proposed to improve radar jamming recognition accuracy with noisy samples. CL learning and a cleansing module are successively utilized to detect and remove potentially noisy samples. Thus, the adverse influence of noisy samples is mitigated. Additionally, semi-supervised learning (SSL) TR, called RadarSSL-PL-TR, is proposed to boost recognition performance under unlabeled samples via pseudo labels (PLs). Network generalization is improved by training with pseudo-labeling unlabeled samples. Moreover, the RadarSSL-PL-S-TR is proposed to further promote recognition performance, where a selection module identifies reliable pseudo-labeling samples. The experimental results show that the proposed RadarCL-TR and RadarSSL-PL-S-TR outperform comparison methods in recognition accuracy by at least

7.07 %

and

6.17 %

with noisy and unlabeled samples, respectively. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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23 pages, 4446 KiB

Open AccessArticle

Co-Frequency Interference Suppression of Integrated Detection and Jamming System Based on 2D Sparse Recovery

by Shiyuan Zhang, Xingyu Lu, Ke Tan, Huabin Yan, Jianchao Yang, Zheng Dai and Hong Gu

Remote Sens. 2024, 16(13), 2325; https://doi.org/10.3390/rs16132325 - 26 Jun 2024

Cited by 2 | Viewed by 1372

Abstract

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, [...] Read more.

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed-

l_{0}

norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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14 pages, 4673 KiB

Open AccessArticle

Experimental Evaluation of a MIMO Radar Performance for ADAS Application

by Federico Dios, Sergio Torres-Benito, Jose A. Lázaro, Josep R. Casas, Jorge Pinazo and Adolfo Lerín

Telecom 2024, 5(3), 508-521; https://doi.org/10.3390/telecom5030026 - 24 Jun 2024

Viewed by 1731

Abstract

Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, trees, street furniture, and traffic [...] Read more.

Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, trees, street furniture, and traffic signs) makes it the most economical complement to the cameras in the visible spectrum in order to give the correct depth to scenes. From the echoes obtained by the radar, some data fusion algorithms will try to locate each object in its correct place within the space surrounding the vehicle. In any case, the usefulness of the radar will be determined by several performance parameters, such as its average error in distance, the maximum errors, and the number of echoes per second it can provide. In this work, we have tested experimentally the AWR1843 MIMO radar from Texas Instruments to measure those parameters. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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13 pages, 1897 KiB

Open AccessArticle

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

by Xiaoyu Lan, Jingyi Hu, Yudi Zhang, Shuang Ma and Ye Tian

Electronics 2024, 13(12), 2386; https://doi.org/10.3390/electronics13122386 - 18 Jun 2024

Cited by 1 | Viewed by 1203

Abstract

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive [...] Read more.

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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27 pages, 3604 KiB

Open AccessArticle

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

by Chenxi Zhang, Yishi Xu, Wenchao Chen, Bo Chen, Chang Gao and Hongwei Liu

Remote Sens. 2024, 16(12), 2199; https://doi.org/10.3390/rs16122199 - 17 Jun 2024

Cited by 1 | Viewed by 1630

Abstract

Traditional radar target detectors, which are model-driven, often suffer remarkable performance degradation in complex clutter environments due to the weakness in modeling the unpredictable clutter. Deep learning (DL) methods, which are data-driven, have been introduced into the field of radar target detection (RTD) [...] Read more.

Traditional radar target detectors, which are model-driven, often suffer remarkable performance degradation in complex clutter environments due to the weakness in modeling the unpredictable clutter. Deep learning (DL) methods, which are data-driven, have been introduced into the field of radar target detection (RTD) since their intrinsic non-linear feature extraction ability can enhance the separability between targets and the clutter. However, existing DL-based detectors are unattractive since they require a large amount of independent and identically distributed (i.i.d.) training samples of target tasks and fail to be generalized to the other new tasks. Given this issue, incorporating the strategy of meta-learning, we reformulate the RTD task as a few-shot classification problem and develop the Inter-frame Contrastive Learning-Based Meta Detector (IfCMD) to generalize to the new task efficiently with only a few samples. Moreover, to further separate targets from the clutter, we equip our model with Siamese architecture and introduce the supervised contrastive loss into the proposed model to explore hard negative samples, which have the targets overwhelmed by the clutter in the Doppler domain. Experimental results on simulated data demonstrate competitive detection performance for moving targets and superior generalization ability for new tasks of the proposed method. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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24 pages, 4569 KiB

Open AccessArticle

Spatial Spectrum Estimation of Weak Scattering Wave Signal in Range-Doppler Domain

by Hang Xu, Hong Ma, Li Wang, Jiang Jin, Hua Zhang and Xiaodong Liu

Remote Sens. 2024, 16(12), 2186; https://doi.org/10.3390/rs16122186 - 16 Jun 2024

Viewed by 980

Abstract

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain [...] Read more.

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 7622 KiB

Open AccessArticle

Variable Doppler Starting Point Keystone Transform for Radar Maneuvering Target Detection

by Wei Jia, Yuan Feng, Xingshuai Qiao, Tianrun Wang and Tao Shan

Remote Sens. 2024, 16(12), 2129; https://doi.org/10.3390/rs16122129 - 12 Jun 2024

Viewed by 1032

Abstract

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by [...] Read more.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address the aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving computational efficiency. Finally, the simulation and real data experiments demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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18 pages, 6416 KiB

Open AccessArticle

Frequency Diversity Array Radar and Jammer Intelligent Frequency Domain Power Countermeasures Based on Multi-Agent Reinforcement Learning

by Changlin Zhou, Chunyang Wang, Lei Bao, Xianzhong Gao, Jian Gong and Ming Tan

Remote Sens. 2024, 16(12), 2127; https://doi.org/10.3390/rs16122127 - 12 Jun 2024

Viewed by 1133

Abstract

With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space [...] Read more.

With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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25 pages, 13526 KiB

Open AccessArticle

Polarization Characteristics Distortion for L-Band Fully Polarimetric Radar Subject to Magnetized Plasma Sheath

by Wei Guo, Yanpeng Hu, Fangfang Shen and Peng Xiao

Remote Sens. 2024, 16(12), 2061; https://doi.org/10.3390/rs16122061 - 7 Jun 2024

Viewed by 1201

Abstract

High-velocity reentry objects suffer from plasma sheath during reentry through the atmosphere, which affects the propagation characteristics of radar signals. The existing research mainly focuses on the time-frequency characteristics of radar signals, neglecting the polarization within the geomagnetic environment. In this article, the [...] Read more.

High-velocity reentry objects suffer from plasma sheath during reentry through the atmosphere, which affects the propagation characteristics of radar signals. The existing research mainly focuses on the time-frequency characteristics of radar signals, neglecting the polarization within the geomagnetic environment. In this article, the distortion of polarization characteristics for L-band fully polarimetric radar is analyzed, and the influence of the geomagnetic field is evaluated. Based on the Appleton–Hartree formula, the refractive index of the plasma sheath considering the geomagnetic field is derived and analyzed. The error model for the polarization deflection (PD) of radar waves is then established based on the phase screen model. The magnetized plasma sheath causes the deflection of the polarization plane for the radar signal, leading to distortion in the polarization characteristics and the attenuation of the echo amplitude. Considering the typical parameters of the plasma sheath, the influences of the electron density, collision frequency, the geomagnetic field and the radar frequency are analyzed quantitatively. Specifically, the PD anomaly phenomenon is analyzed and the corresponding analytical result of radar frequency is also derived. The relationship between the geomagnetic field and the PD, as well as the attenuation, is considered to be approximately linear. The absorption attenuation is primarily influenced by collision frequency and is immune to the geomagnetic field. In addition, the increasing electron density expands them, whereas the radar frequency and the collision frequency have the opposite effect. Simulations with real SAR data from ALOS-2 demonstrate the distortions resulting from the magnetized plasma sheath on the radar echoes in an L-band fully polarimetric radar system. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 1006 KiB

Open AccessArticle

Parameter Extraction of Accelerated Moving Targets under Non-Quasi-Axial Incidence Conditions Based on Vortex Electromagnetic Wave Radar

by Lingling Zhang, Yongzhong Zhu, Yijun Chen, Wenxuan Xie and Hang Yuan

Remote Sens. 2024, 16(11), 1931; https://doi.org/10.3390/rs16111931 - 27 May 2024

Viewed by 1072

Abstract

Vortex electromagnetic wave radar carrying orbital angular momentum can compensate for the deficiency of planar electromagnetic wave radar in detecting motion parameters perpendicular to the direction of electromagnetic wave propagation, thus providing more information for target recognition, which has become a hot research [...] Read more.

Vortex electromagnetic wave radar carrying orbital angular momentum can compensate for the deficiency of planar electromagnetic wave radar in detecting motion parameters perpendicular to the direction of electromagnetic wave propagation, thus providing more information for target recognition, which has become a hot research field in recent years. However, existing research makes it difficult to obtain the acceleration and rotation centers of targets under non-quasi-axial incidence conditions of vortex electromagnetic waves. Based on this, this article proposes a variable speed motion target parameter extraction method that combines single element and total element echoes. This method can achieve three-dimensional information extraction of radar targets based on a uniform circular array (UCA). Firstly, we establish a non-quasi-axis detection echo model for variable-speed moving targets and extract echoes from different array elements. Then, a single element echo is used to extract the target’s range slow time profile and obtain the target’s rotation center z coordinate. We further utilize the target linear, angular Doppler frequency shift extremum, and median information to extract parameters such as target acceleration, tilt angle, rotation radius, and rotation center x and y coordinates. We analyzed the impact of different signal-to-noise ratios and motion states on parameter extraction. The simulation results have verified the effectiveness of the proposed algorithm. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 4981 KiB

Open AccessArticle

A Segmented Sliding Window Reference Signal Reconstruction Method Based on Fuzzy C-Means

by Haobo Liang, Yuan Feng, Yushi Zhang, Xingshuai Qiao, Zhi Wang and Tao Shan

Remote Sens. 2024, 16(10), 1813; https://doi.org/10.3390/rs16101813 - 20 May 2024

Cited by 2 | Viewed by 1266

Abstract

Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented [...] Read more.

Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Multimedia Broadcasting (DTMB) signals, this article proposes a novel segmented sliding window reference signal reconstruction method based on Fuzzy C-Means (FCM). By partitioning the reference signals based on the structure of DTMB signal frames, this approach compensates for frequency offset and sample rate deviation individually for each segment. Additionally, FCM clustering is utilized for symbol mapping reconstruction. Both simulation and experimental results show that the proposed method significantly suppresses constellation diagram divergence and phase rotation, increases the adaptive cancellation gain and signal-to-noise ratio (SNR), and in the meantime reduces the computation cost. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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12 pages, 4945 KiB

Open AccessTechnical Note

A Signal Matching Method of In-Orbit Calibration of Altimeter in Tracking Mode Based on Transponder

by Qingyu Fang, Wei Guo, Caiyun Wang, Peng Liu, Te Wang, Sijia Han, Shijie Yang, Yufei Zhang, Hailong Peng, Chaofei Ma and Bo Mu

Remote Sens. 2024, 16(10), 1682; https://doi.org/10.3390/rs16101682 - 9 May 2024

Viewed by 1107

Abstract

In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding [...] Read more.

In this paper, a matching method for altimeter and transponder signals in Sub-optimal Maximum Likelihood Estimate (SMLE) tracking mode is proposed. In the in-orbit calibration of the altimeter in SMLE tracking mode using the reconstructive transponder, it is necessary to separate the forwarding signal from the ground echo signal. At the same time, the fluctuations in the received signal of the altimeter, which are caused by the forwarding signal of the transponder, can be eliminated. The transponder generates a bias when measuring the arrival time of the transmitting signal from the altimeter and embeds this bias in both the transponder-recorded data and the altimeter-recorded data. Therefore, the two sets of data have one-to-one correspondence, and they are superimposed using the sliding sum method. Moreover, the distance between the altimeter and the transponder is a parabolic geometric relationship, and the outliers are eliminated by the fitting error minimization decision, and the transponder signal is separated from the ground echo. The final altimeter transmitting–receiving signal path is obtained. Furthermore, the principles underlying this method can be used for any transponder that can adjust the response signal delay during calibration. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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25 pages, 856 KiB

Open AccessArticle

Range-Spread Target Detection Networks Using HRRPs

by Yishan Ye, Zhenmiao Deng, Pingping Pan and Wei He

Remote Sens. 2024, 16(10), 1667; https://doi.org/10.3390/rs16101667 - 8 May 2024

Viewed by 1454

Abstract

Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector [...] Read more.

Range-spread target (RST) detection is an important issue for high-resolution radar (HRR). Traditional detectors relying on manually designed detection statistics have their performance limitations. Therefore, in this work, two deep learning-based detectors are proposed for RST detection using HRRPs, i.e., an NLS detector and DFCW detector. The NLS detector leverages domain knowledge from the traditional detector, treating the input HRRP as a low-level feature vector for target detection. An interpretable NLS module is designed to perform noise reduction for the input HRRP. The DFCW detector takes advantage of the extracted high-level feature map of the input HRRP to improve detection performance. It incorporates a feature cross-weighting module for element-wise feature weighting within the feature map, considering the channel and spatial information jointly. Additionally, a nonlinear accumulation module is proposed to replace the conventional noncoherent accumulation operation in the double-HRRP detection scenario. Considering the influence of the target spread characteristic on detector performance, signal sparseness is introduced as a measure and used to assist in generating two datasets, i.e., a simulated dataset and measured dataset incorporating real target echoes. Experiments based on the two datasets are conducted to confirm the contribution of the designed modules to detector performance. The effectiveness of the two proposed detectors is verified through performance comparison with traditional and deep learning-based detectors. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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24 pages, 8326 KiB

Open AccessArticle

High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l₁ Minimization for Laser Radar

by Min Xue, Mengdao Xing, Yuexin Gao, Jixiang Fu, Zhixin Wu and Wangshuo Tang

Remote Sens. 2024, 16(9), 1647; https://doi.org/10.3390/rs16091647 - 6 May 2024

Viewed by 1304

Abstract

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the [...] Read more.

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l₁ minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l₁ minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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19 pages, 5244 KiB

Open AccessArticle

Trajectory Deviation Estimation Method for UAV-Borne Through-Wall Radar

by Luying Chen, Xiaolu Zeng, Shichao Zhong, Junbo Gong and Xiaopeng Yang

Remote Sens. 2024, 16(9), 1593; https://doi.org/10.3390/rs16091593 - 30 Apr 2024

Cited by 6 | Viewed by 1273

Abstract

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation [...] Read more.

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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17 pages, 2065 KiB

Open AccessArticle

Radar Error Correction Method Based on Improved Sparrow Search Algorithm

by Yifei Liu, Zhangsong Shi, Bing Fu and Huihui Xu

Appl. Sci. 2024, 14(9), 3714; https://doi.org/10.3390/app14093714 - 26 Apr 2024

Viewed by 1384

Abstract

Aiming at the problem of the limited application range and low accuracy of existing radar calibration methods, this paper studies the radar calibration method based on cooperative targets, and establishes the integrated radar measurement error model. Then, the improved sparrow search algorithm (ISSA) [...] Read more.

Aiming at the problem of the limited application range and low accuracy of existing radar calibration methods, this paper studies the radar calibration method based on cooperative targets, and establishes the integrated radar measurement error model. Then, the improved sparrow search algorithm (ISSA) is used to estimate the systematic error, so as to avoid the loss of partial accuracy caused by the process of approximating the nonlinear equation to the linear equation, thus improving the radar calibration effect. The sparrow search algorithm (SSA) is improved through integrating various strategies, and the convergence speed and stability of the algorithm are also improved. The simulation results show that the ISSA can solve radar systematic errors more accurately than the generalized least square method, Kalman filter, and SSA. It takes less time the than SSA and has a certain stability and real-time performance. The radar measurement error after correction is obviously smaller than that before correction, indicating that the proposed method is feasible and effective. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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19 pages, 10571 KiB

Open AccessArticle

Carrier-Free Ultra-Wideband Sensor Target Recognition in the Jungle Environment

by Jianchao Li, Shuning Zhang, Lingzhi Zhu, Si Chen, Linsheng Hou, Xiaoxiong Li and Kuiyu Chen

Remote Sens. 2024, 16(9), 1549; https://doi.org/10.3390/rs16091549 - 26 Apr 2024

Cited by 1 | Viewed by 1200

Abstract

Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on [...] Read more.

Carrier-free ultra-wideband sensors have high penetrability anti-jamming solid ability, which is not easily affected by the external environment, such as weather. Also, it has good performance in the complex jungle environment. In this paper, we propose a jungle vehicle identification system based on a carrier-free ultra-wideband sensor. Firstly, a composite jungle environment with the target vehicle is modeled. From this model, the simulation obtains time-domain echoes under the excitation of carrier-free ultra-wideband sensor signals in different orientations. Secondly, the time-domain signals are transformed into MTF images through the Markov transfer field to show the statistical characteristics of the time-domain echoes. At the same time, we propose an improved RepVGG network. The structure of the RepVGG network contains five stages, which consist of several RepVGG Blocks. Each RepVGG Block is created by combining convolutional kernels of different sizes using a weighted sum. We add the self-attention module to the output of stage 0 to improve the ability to extract the features of the MTF map and better capture the complex relationship between characteristics during training. In addition, a self-attention module is added before the linear layer classification output in stage 4 to improve the classification accuracy of the network. Moreover, a combined cross-entropy loss and sparsity penalty loss function helps enhance the performance and accuracy of the network. The experimental results show that the system can recognize jungle vehicle targets well. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 6640 KiB

Open AccessArticle

A Fast Factorized Back-Projection Algorithm Based on Range Block Division for Stripmap SAR

by Yawei Wu, Binbin Li, Bo Zhao and Xiaojun Liu

Electronics 2024, 13(8), 1584; https://doi.org/10.3390/electronics13081584 - 22 Apr 2024

Viewed by 2065

Abstract

Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm’s efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining [...] Read more.

Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm’s efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm’s principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm’s efficiency in stripmap SAR imaging. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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19 pages, 4696 KiB

Open AccessArticle

Two-Dimensional Target Localization Approach via a Closed-Form Solution Using Range Difference Measurements Based on Pentagram Array

by Mohammed Khalafalla, Kaili Jiang, Kailun Tian, Hancong Feng, Ying Xiong and Bin Tang

Remote Sens. 2024, 16(8), 1370; https://doi.org/10.3390/rs16081370 - 12 Apr 2024

Cited by 3 | Viewed by 2160

Abstract

This paper presents a simple and fast closed-form solution approach for two-dimensional (2D) target localization using range difference (RD) measurements. The formulation of the localization problem is derived using a pentagram array. The target position is determined using passive radar measurements (RDs) between [...] Read more.

This paper presents a simple and fast closed-form solution approach for two-dimensional (2D) target localization using range difference (RD) measurements. The formulation of the localization problem is derived using a pentagram array. The target position is determined using passive radar measurements (RDs) between the target and the (

N + 1 = 10

) receivers’ locations. The method facilitates the problem of target position and can be used as a counter-parallel method for spherical interpolation (SI) and spherical intersection (SX) methods in time difference of arrival (TDOA) and radar systems. The performance of the method is examined in 2D target localization using numerical analysis under the distribution of receivers in the pentagram array. The simulations are conducted using four different far-distance targets and comparatively large-area distributed receivers. The RD measurements were distorted by two different values of Gaussian errors based on ionosphedriec time delays of 20 and 50 nsec owing to the different receivers’ positions. The findings highly verified the validity of the method for addressing the problem of target localization. Additionally, a theoretical accuracy study of the method is given, which solely relies on the RD measurements. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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22 pages, 5971 KiB

Open AccessArticle

Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

by Langhuan Geng, Yong Li, Limeng Dong, Yumei Tan and Wei Cheng

Remote Sens. 2024, 16(8), 1364; https://doi.org/10.3390/rs16081364 - 12 Apr 2024

Cited by 3 | Viewed by 1300

Abstract

Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and [...] Read more.

Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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20 pages, 11171 KiB

Open AccessTechnical Note

Monitoring Dynamically Changing Migratory Flocks Using an Algebraic Graph Theory-Based Clustering Algorithm

by Qi Jiang, Rui Wang, Wenyuan Zhang, Longxiang Jiao, Weidong Li, Chunfeng Wu and Cheng Hu

Remote Sens. 2024, 16(7), 1215; https://doi.org/10.3390/rs16071215 - 29 Mar 2024

Viewed by 1122

Abstract

Migration flocks have different forms, including single individuals, formations, and irregular clusters. The shape of a flock can change swiftly over time. The real-time clustering of multiple groups with different characteristics is crucial for the monitoring of dynamically changing migratory flocks. Traditional clustering [...] Read more.

Migration flocks have different forms, including single individuals, formations, and irregular clusters. The shape of a flock can change swiftly over time. The real-time clustering of multiple groups with different characteristics is crucial for the monitoring of dynamically changing migratory flocks. Traditional clustering algorithms need to set various prior parameters, including the number of groups, the number of nearest neighbors, or the minimum number of individuals. However, flocks may display complex group behaviors (splitting, combination, etc.), which complicate the choice and adjustment of the parameters. This paper uses a real-time clustering-based method that utilizes concepts from the algebraic graph theory. The connected graph is used to describe the spatial relationship between the targets. The similarity matrix is calculated, and the problem of group clustering is equivalent to the extraction of the partitioned matrices within. This method needs only one prior parameter (the similarity distance) and is adaptive to the group’s splitting and combination. Two modifications are proposed to reduce the computation burden. First, the similarity distance can be broadened to reduce the exponent of the similarity matrix. Second, the omni-directional measurements are divided into multiple sectors to reduce the dimension of the similarity matrix. Finally, the effectiveness of the proposed method is verified using the experimental results using real radar data. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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16 pages, 6530 KiB

Open AccessArticle

Specific Emitter Identification through Multi-Domain Mixed Kernel Canonical Correlation Analysis

by Jian Chen, Shengyong Li, Jianchi Qi and Hongke Li

Electronics 2024, 13(7), 1173; https://doi.org/10.3390/electronics13071173 - 22 Mar 2024

Cited by 1 | Viewed by 1124

Abstract

Radar specific emitter identification (SEI) involves extracting distinct fingerprints from radar signals to precisely attribute them to corresponding radar transmitters. In view of the limited characterization of fingerprint information by single-domain features, this paper proposes the utilization of multi-domain mixed kernel canonical correlation [...] Read more.

Radar specific emitter identification (SEI) involves extracting distinct fingerprints from radar signals to precisely attribute them to corresponding radar transmitters. In view of the limited characterization of fingerprint information by single-domain features, this paper proposes the utilization of multi-domain mixed kernel canonical correlation analysis for radar SEI. Initially, leveraging the complementarity across diverse feature domains, fingerprint features are extracted from four distinct domains including: envelope feature, spectrum feature, short-time Fourier transform and ambiguity function. Subsequently, kernel canonical correlation analysis is employed to amalgamate the correlation characteristics inherent in multi-domain data. Considering the insufficient of a single kernel function with only interpolation or extrapolation ability, we adopt mixed kernel to improve the projection ability of the kernel function. Experimental results substantiate that the proposed feature fusion approach maximizes the complementarity of multiple features while reducing feature dimensionality. The method achieves an accuracy of up to 95% in experiments, thereby enhancing the efficacy of radar SEI. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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12 pages, 3875 KiB

Open AccessArticle

The Data Compression Method and FPGA Implementation in the Mars Rover Subsurface-Penetrating Radar on the Tianwen-1 Mission

by Shaoxiang Shen, Xiaolei Hua and Bin Zhou

Electronics 2024, 13(6), 1008; https://doi.org/10.3390/electronics13061008 - 7 Mar 2024

Viewed by 1420

Abstract

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China’s first Mars exploration mission has put forward a demand for data compression for all scientific [...] Read more.

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China’s first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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29 pages, 733 KiB

Open AccessArticle

Radar Reconnaissance Pulse-Splitting Modeling and Detection Method

by Ronghua Guo, Yang-Yang Dong, Lidong Zhang, Chunxi Dong, Dan Bao, Wenbo Li and Zhiyuan Li

Remote Sens. 2024, 16(3), 521; https://doi.org/10.3390/rs16030521 - 29 Jan 2024

Cited by 1 | Viewed by 1478

Abstract

When radar receivers adopt digital channelization, it is prone to generating a cross-channel split signal, the rabbit ear effect, and a transition band repeated signal, leading to errors in radar signal sorting or identification. The pulse-splitting model and detection method proposed in this [...] Read more.

When radar receivers adopt digital channelization, it is prone to generating a cross-channel split signal, the rabbit ear effect, and a transition band repeated signal, leading to errors in radar signal sorting or identification. The pulse-splitting model and detection method proposed in this paper can model split pulses and identify them in radar pulse streams, facilitating the merging of split pulses to enhance sorting and identification performance. Firstly, the mechanism of splitting pulse generation is deeply analyzed, and the splitting site theory is proposed. Then, the split pulse signal model and the split pulse number statistical model based on geometric distribution are constructed, which are used to guide the construction of simulation data of split pulse flow with different characteristics. Furthermore, a time-domain convergence degree (TCD) index is proposed to characterize the pulse split phenomenon. At the same time, in order to avoid a large number of threshold searching problems in pulse-splitting detection, an empirical formula for the pulse-splitting detection threshold based on the TCD is given to quickly determine whether there is a pulse train split problem. The selected measured radar pulse stream is verified to follow a geometric distribution at a significance level of 0.05. The proposed method achieved a detection accuracy of at least 99.55% on the simulation dataset and at least 95.68% on the experimental dataset, validating the rationality of pulse-splitting modeling and the effectiveness of the detection method. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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18 pages, 3425 KiB

Open AccessArticle

A Novel Real-Time Processing Wideband Waveform Generator of Airborne Synthetic Aperture Radar

by Dongxu Chen, Tingcun Wei, Gaoang Li, Jie Feng, Jialong Zeng, Xudong Yang and Zhongjun Yu

Remote Sens. 2024, 16(3), 496; https://doi.org/10.3390/rs16030496 - 27 Jan 2024

Cited by 2 | Viewed by 1751

Abstract

This paper investigates a real-time process generator of wideband signals, which calculates waveforms in a field-programmable gate array (FPGA) using the high-level synthesis (HLS) method. To obtain high-resolution and wide-swath images, the generator must produce multiple modes of large time-bandwidth product (TBP) linear [...] Read more.

This paper investigates a real-time process generator of wideband signals, which calculates waveforms in a field-programmable gate array (FPGA) using the high-level synthesis (HLS) method. To obtain high-resolution and wide-swath images, the generator must produce multiple modes of large time-bandwidth product (TBP) linear frequency modulation (LFM) signals. However, the conventional storage method is unrealistic as it requires huge storage resources to save pre-computed waveforms. Therefore, this paper proposes a novel processing approach that calculates waveforms in real-time based simply on parameters such as the sampling frequency, bandwidth, and time width. Additionally, this paper implements predistortion through the polynomial curve to approximate phase errors of the system. The parallelizing process in the FPGA is necessary to satisfy the high-speed requirement of a digital-to-analog converter (DAC); however, repeatedly multiplexing real-time calculation consumes extensive logic and DSP resources, potentially exceeding FPGA limitations. To address this, this paper proposes a piecewise linear algorithm to conserve resources, which processes the polynomial only once, acquires the difference in two adjacent values through the register and pipeline, and then adds this increment to facilitate parallel computations. The performance of this proposed generator is validated through simulation and implemented in experiments with an X-band airborne SAR system. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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20 pages, 5606 KiB

Open AccessArticle

A Focusing Method of Buildings for Airborne Circular SAR

by Dong Feng, Daoxiang An, Jian Wang, Leping Chen and Xiaotao Huang

Remote Sens. 2024, 16(2), 253; https://doi.org/10.3390/rs16020253 - 9 Jan 2024

Cited by 3 | Viewed by 1465

Abstract

Airborne circular synthetic aperture radar (CSAR) can realize high-resolution imaging of the scene over 360 degrees azimuth angle variation. Aiming at the problem of focusing of buildings for the airborne CSAR, this paper first analyzes the phase errors of CSAR buildings focusing in [...] Read more.

Airborne circular synthetic aperture radar (CSAR) can realize high-resolution imaging of the scene over 360 degrees azimuth angle variation. Aiming at the problem of focusing of buildings for the airborne CSAR, this paper first analyzes the phase errors of CSAR buildings focusing in detail, and the analytic relationship between the scatterer height and azimuth focusing quality is deduced. Then, a focusing method of CSAR buildings based on the back projection algorithm is proposed. This method adopts the processing strategy of multi-layers imaging, and it is able to improve azimuth focusing quality of the buildings which have large height dimension. The proposed method is especially suitable for the high-resolution imaging and monitoring of the urban site with high-rise buildings in the airborne CSAR scenario. The correctness of the theoretical analysis and the validity of the proposed method are verified by using both simulation results and Ku-band airborne CSAR real data processing results. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 33199 KiB

Open AccessArticle

Mining Deformation Monitoring Based on Lutan-1 Monostatic and Bistatic Data

by Yanan Ji, Xiang Zhang, Tao Li, Hongdong Fan, Yaozong Xu, Peizhen Li and Zeming Tian

Remote Sens. 2023, 15(24), 5668; https://doi.org/10.3390/rs15245668 - 8 Dec 2023

Cited by 16 | Viewed by 1946

Abstract

Coal mining leads to surface subsidence, landslides, soil erosion and other problems that seriously threaten the life and property safety of residents in mining areas, and it is urgent to obtain mining subsidence information using high-frequency, high-precision and large-scale monitoring methods. Therefore, this [...] Read more.

Coal mining leads to surface subsidence, landslides, soil erosion and other problems that seriously threaten the life and property safety of residents in mining areas, and it is urgent to obtain mining subsidence information using high-frequency, high-precision and large-scale monitoring methods. Therefore, this paper mainly studies the deformation monitoring of the Datong mining area using Lutan-1 monostatic and bistatic SAR data. Firstly, the latest Lutan-1 bistatic data are used to reconstruct the DSM, and the interferometric calibration method is used to improve the accuracy of the DSM. Then, the surface deformation monitoring of the mining area is implemented by using DInSAR, SBAS-InSAR and Stacking-InSAR with the reconstructed DSM data and Lutan-1 monostatic SAR data. Finally, the deformation monitoring results are compared with the surface deformation results based on the TanDEM data, and both the results are evaluated using the filed leveling data. Taking 20 images covering the Datong mining area as the data sources, the surface deformation results obtained using different InSAR methods in the mining area were quantitatively evaluated and analyzed. The results indicated that: (1) the DSM obtained using the Lutan-1 bistatic SAR data was assessed and demonstrated with the ICESat laser altimetry data an error of 2.8 m, which meets the Chinese 1:50,000 scale DEM cartographic accuracy standard, and the difference analysis with the TanDEM data shows that the terrain changes are mainly distributed in mountainous areas; (2) Due to the improvement in resolution, the registration accuracy of the SAR images and LT-DSM is higher than that of the TanDEM data in the range direction and azimuth direction; (3) Via evaluation with the filed leveling data, it is found that the surface deformation measurement results based on LT-DSM are less affected by terrain, and the accuracy of LT-DSM-SBAS and LT-DSM-DInSAR is improved by 11.5% and 16.3%, respectively, compared with TanDEM-SBAS and TanDEM-DInSAR, which demonstrates the effectiveness of the Lutan-1 bistatic and monostatic data for mine deformation monitoring. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 4287 KiB

Open AccessArticle

A Radar Reflectivity Image Prediction Method: The Spatial MIM + Pix2Pix

by Jianlin Guo, Zhiying Lu, Qin Yan and Jianfeng Zhang

Remote Sens. 2023, 15(23), 5554; https://doi.org/10.3390/rs15235554 - 29 Nov 2023

Cited by 1 | Viewed by 1685

Abstract

Radar reflectivity images have the potential to provide vital information on the development of convective cloud interiors, which can play a critical role in precipitation prediction. However, traditional prediction methods face challenges in preserving the high-frequency component, leading to blurred prediction results. To [...] Read more.

Radar reflectivity images have the potential to provide vital information on the development of convective cloud interiors, which can play a critical role in precipitation prediction. However, traditional prediction methods face challenges in preserving the high-frequency component, leading to blurred prediction results. To address this issue and accurately estimate radar reflectivity intensity, this paper proposes a novel reflectivity image prediction approach based on the Spatial Memory in Memory (Spatial MIM) networks and the Pix2Pix networks. Firstly, a rough radar reflectivity image prediction is made using the Spatial MIM network. Secondly, the prediction results from the Spatial MIM network are fed into the Pix2pix network, which improves the high-frequency component of the predicted image and solves the image blurring issue. Finally, the proposed approach is evaluated using data from Oklahoma in the United States during the second and third quarters of 2021. The experimental results demonstrate that the proposed approach yields more accurate radar reflectivity prediction images. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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23 pages, 8143 KiB

Open AccessArticle

Satellite Velocity Correction Method of Ocean Current Retrieval for a Spaceborne Doppler Scatterometer

by Jingyu Zhang, Xiaolong Dong and Di Zhu

Remote Sens. 2023, 15(23), 5541; https://doi.org/10.3390/rs15235541 - 28 Nov 2023

Viewed by 1286

Abstract

For a spaceborne pencil-beam rotating Doppler scatterometer, its precision in measuring the ocean surface motion depends on the Doppler centroid of the received signals. The Doppler centroid is determined by the relative motion between the scatterometer and the ocean surface. This relative motion [...] Read more.

For a spaceborne pencil-beam rotating Doppler scatterometer, its precision in measuring the ocean surface motion depends on the Doppler centroid of the received signals. The Doppler centroid is determined by the relative motion between the scatterometer and the ocean surface. This relative motion includes contributions from satellite velocity, the phase velocity of resonant Bragg waves, the orbital motions of ocean waves, and the ocean surface current. Subtracting the contribution of the satellite platform velocity from the complex Doppler information is important for the application of a spaceborne Doppler scatterometer in ocean surface current retrieval. In this research, we propose a method for the platform velocity correction influenced by the Doppler centroid offset and analyze the accuracy of this correction method. The method is based on the echoed signal model of a Doppler scatterometer. Our results show that the offset could lead to a measurement offset of up to 0.02 m/s when the beam width was 0.3°. For a 0.6° beam width, the maximum offset was 0.07 m/s. Thus, with the high accuracy of the current spaceborne sensors’ measurement, the offset can be accurately eliminated. In future applications and data processing algorithms, this effect should be considered. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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15 pages, 2442 KiB

Open AccessArticle

A Novel Spatial–Temporal Network for Gait Recognition Using Millimeter-Wave Radar Point Cloud Videos

by Chongrun Ma and Zhenyu Liu

Electronics 2023, 12(23), 4785; https://doi.org/10.3390/electronics12234785 - 26 Nov 2023

Cited by 5 | Viewed by 2013

Abstract

Gait recognition is a behavioral biometric technology that aims to identify individuals through their manner of walking. Compared with vision and wearable solutions, millimeter-wave (mmWave)-radar-based gait recognition has drawn attention because radar sensing is privacy-preserving and non-contact. However, it is challenging to capture [...] Read more.

Gait recognition is a behavioral biometric technology that aims to identify individuals through their manner of walking. Compared with vision and wearable solutions, millimeter-wave (mmWave)-radar-based gait recognition has drawn attention because radar sensing is privacy-preserving and non-contact. However, it is challenging to capture the motion dynamics of walking people from mmWave radar signals, which is crucial for robust gait recognition. In this study, a novel spatial–temporal gait recognition network based on mmWave radar is proposed to address this problem. First, a four-dimensional (4D) radar point cloud video (RPCV) was introduced to characterize human walking patterns. Then, a PointNet block was utilized to extract spatial features from the radar point clouds in each frame. Finally, a Transformer layer was applied for the spatial–temporal modeling of the 4D RPCVs, capturing walking motion information, followed by fully connected layers to output the identification results. The experimental results demonstrated the superiority of the proposed network over mainstream networks, which achieved the best human identification performance on a dataset of 15 volunteers. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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20 pages, 2363 KiB

Open AccessArticle

RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification

by Rui Yang, Yingbo Zhao and Yuan Shi

Appl. Sci. 2023, 13(22), 12511; https://doi.org/10.3390/app132212511 - 20 Nov 2023

Cited by 2 | Viewed by 1243

Abstract

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To [...] Read more.

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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15 pages, 1852 KiB

Open AccessTechnical Note

Clutter Covariance Matrix Estimation for Radar Adaptive Detection Based on a Complex-Valued Convolutional Neural Network

by Naixin Kang, Zheran Shang, Weijian Liu and Xiaotao Huang

Remote Sens. 2023, 15(22), 5367; https://doi.org/10.3390/rs15225367 - 15 Nov 2023

Cited by 2 | Viewed by 1787

Abstract

In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models. Therefore, we resort to a data-driven deep-learning [...] Read more.

In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models. Therefore, we resort to a data-driven deep-learning method and propose a covariance matrix estimation method based on a complex-valued convolutional neural network (CV-CNN). Moreover, a real-valued (RV) network with the same framework as the proposed CV network is also constructed to serve as a natural competitor. The obtained clutter covariance matrix estimation based on the network is applied to the adaptive normalized matched filter (ANMF) detector for performance assessment. The detection results via both simulated and real sea clutter illustrate that the estimator based on CV-CNN outperforms other traditional model-based estimators as well as its RV competitor in terms of probability of detection (PD). Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 8611 KiB

Open AccessArticle

3-D Millimeter Wave Fast Imaging Technique Based on 2-D SISO/MIMO Array

by Bo Lin, Yubing Yuan, Yicai Ji, Chao Li, Xiaojun Liu and Guangyou Fang

Remote Sens. 2023, 15(19), 4834; https://doi.org/10.3390/rs15194834 - 5 Oct 2023

Cited by 3 | Viewed by 2263

Abstract

In this article, a novel three-dimensional (3-D) imaging method based on the range decomposing algorithm (RDA) is proposed for millimeter wave imaging. We combined it with binomial theory and we derive the theoretical formulation of RDA applied to single-input–single-output (SISO)/multiple-input–multiple-output (MIMO) array; meanwhile, [...] Read more.

In this article, a novel three-dimensional (3-D) imaging method based on the range decomposing algorithm (RDA) is proposed for millimeter wave imaging. We combined it with binomial theory and we derive the theoretical formulation of RDA applied to single-input–single-output (SISO)/multiple-input–multiple-output (MIMO) array; meanwhile, its computational complexity and computational error are analyzed. Compared to the classical Fourier algorithm, such as the range migration algorithm (RMA) and the phase shift migration (PSM), the proposed algorithm can replace the time-consuming interpolation and accumulation operations with reasonable approximations and transformations offering a more efficient approach, while maintaining the image quality. In addition, a method based on RDA which is applicable to the transformation between MIMO and SISO, is proposed to further enhance the processing efficiency. Proof-of-principle simulation using echo data collected from a large number of antennas, verifies that the proposed algorithm has higher efficiency. In order to better verify the feasibility of the proposed algorithm, a scanning prototype located in the millimeter wave band is designed. The experimental results of different targets demonstrate that the proposed algorithm achieves significantly higher reconstruction efficiency when compared to the traditional algorithms. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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16 pages, 4456 KiB

Open AccessArticle

Calibration of Wideband LFM Radars Based on Sliding Window Algorithm

by Hyungwoo Kim, Jinwoo Kim, Jinha Kim, Jaeyoung Choi, Sangpyo Hong, Nammoon Kim and Byungkwan Kim

Electronics 2023, 12(17), 3564; https://doi.org/10.3390/electronics12173564 - 23 Aug 2023

Cited by 1 | Viewed by 1774

Abstract

This paper addresses the challenges of wideband signal beamforming in radar systems and proposes a new calibration method. Due to operating conditions, the frequency-dependent characteristics of the system can be changed, and the amplitude, phase, and time delay error can be generated. The [...] Read more.

This paper addresses the challenges of wideband signal beamforming in radar systems and proposes a new calibration method. Due to operating conditions, the frequency-dependent characteristics of the system can be changed, and the amplitude, phase, and time delay error can be generated. The proposed method is based on the concept of the sliding window algorithm for linear frequency modulated (LFM) signals. To calibrate the frequency-dependent errors from the transceiver and the time delay error from the true time delay elements, the proposed method utilizes the characteristic of the LFM signal. The LFM signal changes its frequency linearly with time, and the frequency domain characteristics of the hardware are presented in time. Therefore, by applying a matched filter to a part of the LFM signal, the frequency-dependent characteristics can be monitored and calibrated. The proposed method is compared with the conventional matched filter-based calibration results and verified by simulation results and beampatterns. Since the proposed method utilizes LFM signal as the calibration tone, the proposed method can be applied to any beamforming systems, not limited to LFM radars. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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25 pages, 4202 KiB

Open AccessArticle

Radar Anti-Jamming Decision-Making Method Based on DDPG-MADDPG Algorithm

by Jingjing Wei, Yinsheng Wei, Lei Yu and Rongqing Xu

Remote Sens. 2023, 15(16), 4046; https://doi.org/10.3390/rs15164046 - 16 Aug 2023

Cited by 13 | Viewed by 2751

Abstract

In the face of smart and varied jamming, intelligent radar anti-jamming technologies are urgently needed. Due to the variety of radar electronic counter-countermeasures (ECCMs), it is necessary to efficiently optimize ECCMs in the high-dimensional knowledge base to ensure that the radar achieves the [...] Read more.

In the face of smart and varied jamming, intelligent radar anti-jamming technologies are urgently needed. Due to the variety of radar electronic counter-countermeasures (ECCMs), it is necessary to efficiently optimize ECCMs in the high-dimensional knowledge base to ensure that the radar achieves the optimal anti-jamming effect. Therefore, an intelligent radar anti-jamming decision-making method based on the deep deterministic policy gradient (DDPG) and the multi-agent deep deterministic policy gradient (MADDPG) (DDPG-MADDPG) algorithm is proposed. Firstly, by establishing a typical working scenario of radar and jamming, we designed the intelligent radar anti-jamming decision-making model, and the anti-jamming decision-making process was formulated. Then, aiming at different jamming modes, we designed the anti-jamming improvement factor and the correlation matrix of jamming and ECCM. They were used to evaluate the jamming suppression performance of ECCMs and to provide feedback for the decision-making algorithm. The decision-making constraints and four different decision-making objectives were designed to verify the performance of the decision-making algorithm. Finally, we designed a DDPG-MADDPG algorithm to generate the anti-jamming strategy. The simulation results showed that the proposed method has excellent robustness and generalization performance. At the same time, it has a shorter convergence time and higher anti-jamming decision making accuracy. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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16 pages, 2503 KiB

Open AccessTechnical Note

Target Recognition in SAR Images Using Complex-Valued Network Guided with Sub-Aperture Decomposition

by Ruonan Wang, Zhaocheng Wang, Yu Chen, Hailong Kang, Feng Luo and Yingxi Liu

Remote Sens. 2023, 15(16), 4031; https://doi.org/10.3390/rs15164031 - 14 Aug 2023

Cited by 6 | Viewed by 2254

Abstract

Synthetic aperture radar (SAR) images have special physical scattering characteristics owing to their unique imaging mechanism. Traditional deep learning algorithms usually extract features from real-valued SAR images in a purely data-driven manner, which may ignore some important physical scattering characteristics and sacrifice some [...] Read more.

Synthetic aperture radar (SAR) images have special physical scattering characteristics owing to their unique imaging mechanism. Traditional deep learning algorithms usually extract features from real-valued SAR images in a purely data-driven manner, which may ignore some important physical scattering characteristics and sacrifice some useful target information in SAR images. This undoubtedly limits the improvement in performance for SAR target recognition. To take full advantage of the physical information contained in SAR images, a complex-valued network guided with sub-aperture decomposition (CGS-Net) for SAR target recognition is proposed. According to the fact that different targets have different physical scattering characteristics at different angles, the sub-aperture decomposition is used to improve accuracy with a multi-task learning strategy. Specifically, the proposed method includes main and auxiliary tasks, which can improve the performance of the main task by learning and sharing useful information from the auxiliary task. Here, the main task is the target recognition task, and the auxiliary task is the target reconstruction task. In addition, a complex-valued network is used to extract the features from the original complex-valued SAR images, which effectively utilizes the amplitude and phase information in SAR images. The experimental results obtained using the MSTAR dataset illustrate that the proposed CGS-Net achieved an accuracy of 99.59% (without transfer learning or data augmentation) for the ten-classes targets, which is superior to the other popular deep learning methods. Moreover, the proposed method has a lightweight network structure, which is suitable for SAR target recognition tasks because SAR images usually lack a large number of labeled data. Here, the experimental results obtained using the small dataset further demonstrate the excellent performance of the proposed CGS-Net. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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19 pages, 8067 KiB

Open AccessArticle

Determination of Meteor Vector Velocity Using MU Interferometry Measurements of Head Echoes

by Xin Xie, Zhangyou Chen, Li Wang, Heng Zhou and Xiongbin Wu

Remote Sens. 2023, 15(15), 3784; https://doi.org/10.3390/rs15153784 - 29 Jul 2023

Viewed by 1706

Abstract

A new method for measuring the vector velocity of meteoroids using meteor head echoes is proposed in this study. The lateral velocity is determined by utilizing the phase interference measurement between channels, while the radial velocity is obtained using a conventional Doppler frequency [...] Read more.

A new method for measuring the vector velocity of meteoroids using meteor head echoes is proposed in this study. The lateral velocity is determined by utilizing the phase interference measurement between channels, while the radial velocity is obtained using a conventional Doppler frequency shift measurement. Compared to previous studies, this method does not require multi-site observations and can calculate the vector velocity of meteors in real-time. This paper provides the complete process for the inversion of the meteor vector velocity, detailing the analyzing process using MU radar head echo data. First, the MUSIC algorithm was used to estimate the DOA of the meteor target, which is a parameter required for lateral velocity measurement. Channel calibration is required before this estimation. Next, delay-Doppler matched filter processing was performed on each receiving channel’s data to determine the distance and radial velocity of the meteor target. Subsequently, the lateral velocity component was synthesized using the least squares method from the phase difference rate extracted from the matched filter output results of multiple channel pairs. Then, the vector velocity and trajectory of the meteor could be determined. The method was verified using MU radar head echo data. Different groups of channel pairs were selected for calculating the lateral velocity, and the results were found to be close, demonstrating the self-consistency of the method. Additionally, the calculated vector velocity is consistent with the direction and magnitude of the meteor’s motion trajectory, confirming the feasibility of the proposed approach. The method allows for the observation of more prominent characteristics of meteoroid motion, providing a more detailed observation capability of velocity variations in other directions than previous methods. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 6823 KiB

Open AccessArticle

Research on an Intra-Pulse Orthogonal Waveform and Methods Resisting Interrupted-Sampling Repeater Jamming within the Same Frequency Band

by Huahua Dai, Yingxiao Zhao, Hanning Su, Zhuang Wang, Qinglong Bao and Jiameng Pan

Remote Sens. 2023, 15(14), 3673; https://doi.org/10.3390/rs15143673 - 23 Jul 2023

Cited by 8 | Viewed by 1539

Abstract

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal [...] Read more.

Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent deception jamming that can generate false target peaks in the range profile and interfere with the detection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by comparing sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discontinuity of the interference signal in the time domain is proposed in this paper. The method proposed in this paper can recognize real and false targets in all ISRJ modes without any prior information, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3 dB loss of pulse compression gain, it can completely suppress different kinds of ISRJ interference when the SNR before pulse compression is higher than −20 dB, with 100% target detection probability. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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23 pages, 7351 KiB

Open AccessArticle

An Hybrid Integration Method-Based Track-before-Detect for High-Speed and High-Maneuvering Targets in Ubiquitous Radar

by Xiangyu Peng, Qiang Song, Yue Zhang and Wei Wang

Remote Sens. 2023, 15(14), 3507; https://doi.org/10.3390/rs15143507 - 12 Jul 2023

Cited by 2 | Viewed by 1539

Abstract

Due to the limited transmission gain of ubiquitous radar systems, it has become necessary to use a long-time coherent integration method for range-Doppler (RD) analysis. However, when the target exhibits high-speed and high-maneuver capabilities, it introduces challenges, such as range migration (RM), Doppler [...] Read more.

Due to the limited transmission gain of ubiquitous radar systems, it has become necessary to use a long-time coherent integration method for range-Doppler (RD) analysis. However, when the target exhibits high-speed and high-maneuver capabilities, it introduces challenges, such as range migration (RM), Doppler frequency migration (DFM), and velocity ambiguity (VA) in the RD domain, thus posing significant difficulties in target detection and tracking. Moreover, the presence of VA further complicates the problem. To address these complexities while maintaining integration efficiency, this study proposes a hybrid integration approach. First, methods called Keystone-transform (KT) and matched filtering processing (MFP) are proposed for compensating for range migration (RM) and velocity ambiguity (VA) in Radar Detection (RD) images. The KT approach is employed to compensate for RM, followed by the generation of matched filters with varying ambiguity numbers. Subsequently, MFP enables the production of multiple RD images covering different but contiguous Doppler frequency ranges. These RD images can be compiled into an extended RD (ERD) image that exhibits an expanded Doppler frequency range. Second, an improved particle-filter (IPF) algorithm is raised to perform incoherent integration among ERD images and to achieve track-before-detect (TBD) for a target. In the IPF, the target state vector is augmented with ambiguous numbers, which are estimated via maximum posterior probability estimation. Then, to compensate for the DFM, a line spread model (LSM) is proposed instead of the point spread model (PSM) used in traditional PF. To evaluate the efficacy of the proposed method, a radar simulator is devised, encompassing comprehensive radar signal processing. The findings demonstrate that the proposed approach achieves a harmonious equilibrium between integration efficiency and computational complexity when it comes to detecting and tracking high-speed and high-maneuvering targets with intricate maneuvers. Furthermore, the algorithm’s effectiveness is authenticated by exploiting ubiquitous radar data. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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19 pages, 686 KiB

Open AccessArticle

An Efficient Algorithm for De-Interleaving Staggered PRI Signals

by Wenhai Cheng, Qunying Zhang, Jiaming Dong, Haiying Wang and Xiaojun Liu

Appl. Sci. 2023, 13(13), 7977; https://doi.org/10.3390/app13137977 - 7 Jul 2023

Viewed by 2125

Abstract

Resolution and mapping bandwidth are the two most important image performance indicators that reflect satellite synthetic aperture radar (SAR) imaging reconnaissance capability. The PRI-staggered signal can simultaneously achieve high resolution in azimuth and wide swath during SAR imaging, and is an important signal [...] Read more.

Resolution and mapping bandwidth are the two most important image performance indicators that reflect satellite synthetic aperture radar (SAR) imaging reconnaissance capability. The PRI-staggered signal can simultaneously achieve high resolution in azimuth and wide swath during SAR imaging, and is an important signal form of SAR. It is important for anti-SAR reconnaissance to de-interleave the staggered PRI signal from the mixed signals. To address the problem that the existing staggered signal de-interleaving algorithms cannot accommodate PRI jitter and are computationally inefficient, this paper proposes an efficient algorithm for de-interleaving staggered PRI signals. A clustering-based square sine wave interpolation method and a threshold criterion are proposed, improving computational efficiency while suppressing interference between sub-PRIs and the frame period of the staggered PRI signal. In addition, a sequence retrieval algorithm incorporating matched filter theory is proposed to improve the separation accuracy of radar pulse sequences. The simulation shows that the novel algorithm can adapt to PRI jitter and de-interleave staggered PRI signals from mixed signals with high efficiency. Compared with the existing staggered signal de-interleaving algorithm, the computational efficiency is improved by an order of magnitude. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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14 pages, 5258 KiB

Open AccessArticle

System Design and Signal Processing in Spaceborne Squint Sliding Spotlight SAR with Sub-Aperture Block-Varying PRF

by Wei Xu, Zhuo Zhang, Pingping Huang, Weixian Tan and Yaolong Qi

Electronics 2023, 12(13), 2835; https://doi.org/10.3390/electronics12132835 - 27 Jun 2023

Cited by 2 | Viewed by 1467

Abstract

To tackle the problems of Doppler spectrum, aliasing caused by azimuth beam scanning and azimuthal serious non-uniform sampling in squint sliding spotlight synthetic aperture radar (SAR) with varying repetition frequency technology, the azimuth sampling method of sub-aperture block-varying pulse repetition frequency (SBV-PRF) is [...] Read more.

To tackle the problems of Doppler spectrum, aliasing caused by azimuth beam scanning and azimuthal serious non-uniform sampling in squint sliding spotlight synthetic aperture radar (SAR) with varying repetition frequency technology, the azimuth sampling method of sub-aperture block-varying pulse repetition frequency (SBV-PRF) is proposed, where the sub-aperture division judgement makes the azimuth acquisition time of each sub-block small enough so that the Doppler bandwidth caused by the Doppler center change can be ignored. Based on the echo signal characteristics of a SBV-PRF transmission scheme, an azimuth pre-processing method combining SBV-PRF transmission scheme with sub-aperture division is proposed. Using this method, de-skewing is first performed on each set of sub-aperture data to eliminate the additional Doppler bandwidth introduced by the squint angle, and then the azimuth signal resampling is performed to ensure different sub-aperture data have the same sampling rate. The SBV-PRF technology reduces the difficulty of azimuth signal pre-processing while ensuring the complete acquisition of the complete echo data of the squint sliding spotlight mode. The effectiveness of the SBV-PRF system design and the signal processing method is verified by the point target echo simulation and imaging simulation results. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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19 pages, 3270 KiB

Open AccessArticle

A Novel Parameter Estimation Method Based on Piecewise Nonlinear Amplitude Transform for the LFM Signal in Impulsive Noise

by Haiying Wang, Qunying Zhang, Wenhai Cheng, Jiaming Dong and Xiaojun Liu

Electronics 2023, 12(11), 2530; https://doi.org/10.3390/electronics12112530 - 3 Jun 2023

Cited by 2 | Viewed by 1532

Abstract

In a complex electromagnetic environment, any noise present generally exhibits strong impulsive characteristics. The performance of existing parameter estimation methods carried out in Gaussian white noise for the linear frequency modulation (LFM) signal degrades or even fails under impulsive noise. This paper proposes [...] Read more.

In a complex electromagnetic environment, any noise present generally exhibits strong impulsive characteristics. The performance of existing parameter estimation methods carried out in Gaussian white noise for the linear frequency modulation (LFM) signal degrades or even fails under impulsive noise. This paper proposes a novel parameter estimation method to address this problem. Firstly, the properties of the piecewise nonlinear amplitude transform (PNAT) are derived. This manuscript verifies that the PNAT can retain phase information of the LFM signal while suppressing the impulsive noise. Subsequently, a new concept known as piecewise nonlinear amplitude transform parametric symmetric instantaneous autocorrelation function (PNAT-PSIAF) is proposed. Based on this concept, a novel method called piecewise nonlinear amplitude transform Lv’s distribution (PNAT-LVD) is proposed to estimate the centroid frequency and chirp rate of the LFM signal. The simulations show that the proposed algorithm can effectively suppress the impulsive noise without prior knowledge of the noise for both the single-component and double-component LFM signal. In addition, two parameters of the LFM signal can be precisely estimated by the proposed method under low generalized signal-to-noise ratios (GSNR). The stronger the impulsive characteristics of the noise, the better the performance of the algorithm. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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18 pages, 2149 KiB

Open AccessArticle

Geometric Configuration Design and Fast Imaging for Multistatic Forward-Looking SAR Based on Wavenumber Spectrum Formation Approach

by Yumeng Liu, Yijing Zhao and Yi Ding

Remote Sens. 2023, 15(11), 2783; https://doi.org/10.3390/rs15112783 - 26 May 2023

Cited by 1 | Viewed by 1777

Abstract

Multistatic forward-looking synthetic aperture radar (Mu-FLSAR) has the potential of high-resolution imaging with short synthetic aperture time, which can improve the transmitter’s survivability, by coherently fusing simultaneously observed measurements of multiple receivers. However, the combined performance of the multiple measurements strictly depends on [...] Read more.

Multistatic forward-looking synthetic aperture radar (Mu-FLSAR) has the potential of high-resolution imaging with short synthetic aperture time, which can improve the transmitter’s survivability, by coherently fusing simultaneously observed measurements of multiple receivers. However, the combined performance of the multiple measurements strictly depends on an appropriate geometric configuration among the transmitter and receivers because the forward-looking application limits the flight directions of receivers. In this paper, to design a geometric configuration for Mu-FLSAR, a wavenumber spectrum formation (WSF) approach is proposed based on the projection relationship between the wavenumber support regions (WSRs) and geometric configuration parameters. On the one hand, the projected pattern of multiple WSRs is deduced, and the relationship between multiple WSRs and the point spread function (PSF) is analyzed. Based on the geometric feature of the kernel WSR, which is formed by the transmitter and the master receiver, and the relationship between the geometric features and the geometric configuration parameters, including synthetic aperture time and azimuthal angle, a WSF method is proposed to visually and quickly deduce the geometric parameter of the salve receivers. On the other hand, based on the designed geometric configuration of Mu-FLSAR, a wavenumber-dependent fast polar format algorithm (WF-PFA) is proposed to efficiently reconstruct the targets relying on the geometric features of WSRs. Simulation results verify the proposed method. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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23 pages, 7196 KiB

Open AccessArticle

Raw Data Simulation of Spaceborne Synthetic Aperture Radar with Accurate Range Model

by Haisheng Li, Junshe An, Xiujie Jiang and Meiyan Lin

Remote Sens. 2023, 15(11), 2705; https://doi.org/10.3390/rs15112705 - 23 May 2023

Cited by 2 | Viewed by 3357

Abstract

Simulated raw data have become an essential tool for testing and assessing system parameters and imaging performance due to the high cost and limited availability of real raw data from spaceborne synthetic aperture radar (SAR). However, with increasing resolution and higher orbit altitudes, [...] Read more.

Simulated raw data have become an essential tool for testing and assessing system parameters and imaging performance due to the high cost and limited availability of real raw data from spaceborne synthetic aperture radar (SAR). However, with increasing resolution and higher orbit altitudes, existing simulation methods fail to generate SAR simulated raw data that closely resemble real raw data. This is due to approximations such as curved orbits, “stop-and-go” assumption, and Earth’s rotation, among other factors. To overcome these challenges, this paper presents an accurate range model with a “nonstop-and-go” configuration for raw data simulation based on existing time-domain simulation methods. We model the SAR echo signal and establish a precise space geometry for spaceborne SAR. Additionally, we precisely identify the target illumination area based on elliptical beams through space coordinate transformation. Finally, the SAR raw data were accurately simulated using high-precision time-domain simulation methods. The accuracy of the proposed model was validated by comparing it with the traditional hyperbolic model and the curved orbit model with “stop-and-go” assumption through image processing of the generated raw data. Through the analysis of point target quality parameters, the errors of various parameters in our distance model compared with the other two models are within 1%. Furthermore, this simulation method can be adapted to simulate raw data of other modes and satellite orbits by adjusting beam control and satellite orbit parameters, respectively. The proposed simulation method demonstrated high accuracy and versatility, thereby providing a valuable contribution to the development of remote sensing technology. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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14 pages, 1267 KiB

Open AccessArticle

Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar

by Young-Keun Yoo, Chae-Won Jung and Hyun-Chool Shin

Appl. Sci. 2023, 13(7), 4468; https://doi.org/10.3390/app13074468 - 31 Mar 2023

Cited by 5 | Viewed by 2926

Abstract

The paper proposes a unsupervised method for detecting the three stages of sleep—wake, rapid eye movement (REM) sleep, and non-REM sleep—using biosignals obtained from a 61 GHz single frequency modulated continuous wave (FMCW) radar. To detect the subject’s sleep stages [...] Read more.

The paper proposes a unsupervised method for detecting the three stages of sleep—wake, rapid eye movement (REM) sleep, and non-REM sleep—using biosignals obtained from a 61 GHz single frequency modulated continuous wave (FMCW) radar. To detect the subject’s sleep stages based on non-learning techniques, the breathing and movement information characteristic of each sleep stage was extracted from the radar signals of the subject acquired in the sleep state and used as the feature factor tailored to the research objective. The experimental results derived from the clinical data obtained in the actual polysomnography (PSG) environment using FMCW radar show an average of 68% similarity to the actual three sleep stages observed in PSG. These results indicate the feasibility of using the FMCW radar sensor as an alternative to the conventional PSG-based method that poses multiple limitations to sleep-stage detection. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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12 pages, 949 KiB

Open AccessCommunication

An Efficient 2D DOA Estimation Algorithm Based on OMP for Rectangular Array

by Chuang Wang, Jianmin Hu, Qunying Zhang and Xinhao Yuan

Electronics 2023, 12(7), 1634; https://doi.org/10.3390/electronics12071634 - 30 Mar 2023

Cited by 5 | Viewed by 2389

Abstract

Recently, orthogonal matching pursuit (OMP) has been widely used in direction of arrival (DOA) studies, which not only greatly improves the resolution of DOA, but can also be applied to single-snapshot and coherent source cases. When applying the OMP algorithm to the rectangular [...] Read more.

Recently, orthogonal matching pursuit (OMP) has been widely used in direction of arrival (DOA) studies, which not only greatly improves the resolution of DOA, but can also be applied to single-snapshot and coherent source cases. When applying the OMP algorithm to the rectangular array DOA of the millimeter-wave radar, it is necessary to reshape the two-dimensional (2D) signal into a long one-dimensional (1D) signal. However, the long 1D signal will greatly increase the number and length of atoms in the complete dictionary of the OMP algorithm, which will greatly increase the amount of computation. Taking advantage of the sparsity of targets in the DOA space, an efficient 2D DOA estimation algorithm based on OMP for rectangular array is proposed. The main idea is to reduce the number of atoms in the complete dictionary of the OMP algorithm, thereby greatly reducing the amount of computation required. A simulation verifies that the efficiency of the proposed algorithm is much higher than the conventional algorithm with almost the same estimation accuracy. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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18 pages, 2147 KiB

Open AccessArticle

An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection

by Wenhai Cheng, Qunying Zhang, Wei Lu, Haiying Wang and Xiaojun Liu

Appl. Sci. 2023, 13(5), 3080; https://doi.org/10.3390/app13053080 - 27 Feb 2023

Cited by 3 | Viewed by 2570

Abstract

Synthetic aperture radar (SAR) is essential for obtaining intelligence in modern information warfare. Wideband chirp signals with a low signal-to-noise ratio (SNR) are widely used in SAR. Intercepting low-SNR wideband chirp signals is of great significance for anti-SAR reconnaissance. Digital channelization technology is [...] Read more.

Synthetic aperture radar (SAR) is essential for obtaining intelligence in modern information warfare. Wideband chirp signals with a low signal-to-noise ratio (SNR) are widely used in SAR. Intercepting low-SNR wideband chirp signals is of great significance for anti-SAR reconnaissance. Digital channelization technology is an effective means to intercept wideband signals. The existing digital channelization methods have the following problems: the contradiction of reception blind zone and signal spectrum aliasing, high computational complexity, and low estimating accuracy for chirp signals with a low SNR. This paper proposes a non-critical sampling digital channelized receiver architecture to intercept chirp signals. The receiver architecture has no blind zone in channel division and no aliasing of signal spectrum in the channel, which can provide reliable instantaneous frequency measurements. An adaptive threshold generation algorithm is proposed to detect signals without prior information. In addition, an improved instantaneous frequency measurement (IFM) algorithm is proposed, improving low SNR chirp signals’ frequency estimation accuracy. Moreover, a simple channel arbitration logic is proposed to complete the cross-channel combination of wideband signals. Simulations show that the proposed receiver architecture is reliable and robust for low SNR and wideband chirp signal detection. When the input SNR is 0 dB, the absolute frequency root-mean-square error (RMSE) of bandwidth and the center frequency is 0.57 MHz and 1.05 MHz, respectively. This frequency accuracy is great for radio frequency (RF) wideband systems. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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21 pages, 5940 KiB

Open AccessArticle

Data-Independent Phase-Only Beamforming of FDA-MIMO Radar for Swarm Interference Suppression

by Geng Chen, Chunyang Wang, Jian Gong, Ming Tan and Yibin Liu

Remote Sens. 2023, 15(4), 1159; https://doi.org/10.3390/rs15041159 - 20 Feb 2023

Cited by 5 | Viewed by 2135

Abstract

This paper proposes two data-independent phase-only beamforming algorithms for frequency diverse array multiple-input multiple-output radar against swarm interference. The proposed strategy can form a deep null at the interference area to achieve swarm interference suppression by tuning the phase of the weight vector, [...] Read more.

This paper proposes two data-independent phase-only beamforming algorithms for frequency diverse array multiple-input multiple-output radar against swarm interference. The proposed strategy can form a deep null at the interference area to achieve swarm interference suppression by tuning the phase of the weight vector, which can effectively reduce the hardware cost of the receiver. Specifically, the first algorithm imposes constant modulus constraint and sidelobe level constraint, and the phase-only weight vector is solved. The second algorithm performs a constant modulus decomposition of the weight vector to obtain two phase-only weight vectors, and uses two parallel phase shifters to synthesize one beamforming weight. Both methods can obtain the phase-only weight to realize suppression for swarm interference. Simulation results demonstrate that our strategy shows superiority in beam shape, output signal-to-interference-noise ratio, and phase shifter quantization performance, and has the potential for use in many applications, such as radar countermeasures and electronic defense. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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28 pages, 12706 KiB

Open AccessArticle

Backscattering Statistics of Indoor Full-Polarization Scatterometric and Synthetic Aperture Radar Measurements of a Rice Field

by Xiangchen Liu, Yun Shao, Kun Li, Zhiqu Liu, Long Liu and Xiulai Xiao

Remote Sens. 2023, 15(4), 965; https://doi.org/10.3390/rs15040965 - 9 Feb 2023

Cited by 2 | Viewed by 1878

Abstract

The backscattering coefficient

σ^{0}

of a rice field is closely related to the amplitude, power, and phase of its radar backscattered signals. An investigation of the statistics of indoor full-polarization scatterometric and synthetic aperture radar (SAR) measurements on rice fields in the [...] Read more.

The backscattering coefficient

σ^{0}

of a rice field is closely related to the amplitude, power, and phase of its radar backscattered signals. An investigation of the statistics of indoor full-polarization scatterometric and synthetic aperture radar (SAR) measurements on rice fields in the Laboratory of Target Microwave Properties (LAMP) is implemented in terms of the amplitude, power, and phase difference of backscattered signals. The validity and accuracy of LAMP measured data are studied and confirmed for the first time. The Rayleigh fading model and phase difference statistical model are both validated by the experimental data. Continuous microwave spectrum is obtained after spatial and frequency averaging over N independent scatterometric samples and full-polarization images are generated by applying a focusing algorithm to the SAR data. Comparisons between scatterometric results and SAR images with three resolutions of rice field scene are conducted with respect to amplitude and co-pol phase difference (CPD) statistics, as well as backscattering coefficients. The results show that the measured statistics of a rice field scene are in good agreement with those calculated by theoretical formulas. Spatial and frequency averaging of scatterometric data can increase N and thus improve the estimation accuracy of the backscattering coefficients. SAR images show a shift to the near range due to the intrinsic height of the rice plants and the probable existence of the double bounce scattering between vertical rice stems and the water surface considering the measurement geometry. The measured amplitude statistics of the SAR images approach a Rayleigh distribution with reduction of the resolution cell size while the size has little effect on the CPD statistics. The differences between backscattering coefficients extracted from the scatterometric data and SAR images confirm a 1-dB calibration accuracy in power of the LAMP measurement system. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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16 pages, 3729 KiB

Open AccessArticle

DOA Estimation Using Deep Neural Network with Angular Sliding Window

by Yang Li, Zanhu Huang, Can Liang, Liang Zhang, Yanhua Wang, Junfu Wang, Yi Zhang and Hongfen Lv

Electronics 2023, 12(4), 824; https://doi.org/10.3390/electronics12040824 - 6 Feb 2023

Cited by 3 | Viewed by 2581

Abstract

Deep neural network (DNN) has shown great potential in direction-of-arrival (DOA) estimation. In high dynamic signal-to-noise (SNR) scenarios, the estimation accuracy of the weaker sources may degrade significantly due to insufficient training samples. This paper proposes a deep neural network framework with sliding [...] Read more.

Deep neural network (DNN) has shown great potential in direction-of-arrival (DOA) estimation. In high dynamic signal-to-noise (SNR) scenarios, the estimation accuracy of the weaker sources may degrade significantly due to insufficient training samples. This paper proposes a deep neural network framework with sliding window operation. The whole field-of-view (FOV) is divided into a series of sub-regions via sliding windows. Each sub-region is assumed to contain one source at most. Thus, the single-source data can be used to train all the networks, alleviating the need for the training samples and the prior information on the number of sources. A detector network and an estimator network are followed for each sub-region, enabling high estimation accuracy and the number of sources. Simulation and real data experiment results show that the proposed method can achieve excellent DOA and source number estimation performance. Specifically, in the real data experiment, the results show that the RMSE of the proposed method reaches 0.071, which is at least 0.03 lower than FFT, MUSIC, ESPRIT, and a deep learning method namely deep convolutional network (DCN), cannot estimate the lower SNR source in high dynamic SNR scenarios. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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20 pages, 2746 KiB

Open AccessArticle

Robust Velocity Dealiasing for Weather Radar Based on Convolutional Neural Networks

by Hyeri Kim and Boonleng Cheong

Remote Sens. 2023, 15(3), 802; https://doi.org/10.3390/rs15030802 - 31 Jan 2023

Cited by 4 | Viewed by 2959

Abstract

Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. A large aliasing range (

r_{a}

) is important for surveillance but a high aliasing velocity (

v_{a}

) is also important to obtain storm dynamics [...] Read more.

Doppler weather radar is an essential tool for monitoring and warning of hazardous weather phenomena. A large aliasing range (

r_{a}

) is important for surveillance but a high aliasing velocity (

v_{a}

) is also important to obtain storm dynamics unambiguously. However, the

r_{a}

and

v_{a}

are inversely related to pulse repetition time. This “Doppler dilemma” is more challenging at shorter wavelengths. The proposed algorithm employs a CNN (convolutional neural network), which is widely used in image classification, to tackle the velocity dealiasing issue. Velocity aliasing can be converted to a classification problem. The velocity field and aliased count can be regarded as the input image and the label, respectively. Through a fit-and-adjust process, the best weights and the biases of the model are determined to minimize a cost function. The proposed method is compared against the traditional region-based method. Both methods show similar performance on mostly filled precipitation. For sparsely filled precipitation; however, the CNN demonstrated better performance since the CNN processes the entire scan at once while the region-based method processes only the limited adjacent area. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)
(This article belongs to the Section AI Remote Sensing)

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21 pages, 787 KiB

Open AccessFeature PaperReview

Multi-Object Tracking with mmWave Radar: A Review

by Andre Pearce, J. Andrew Zhang, Richard Xu and Kai Wu

Electronics 2023, 12(2), 308; https://doi.org/10.3390/electronics12020308 - 6 Jan 2023

Cited by 24 | Viewed by 11603

Abstract

The boundaries of tracking and sensing solutions are continuously being pushed. A stimulation in this field over recent years is exploiting the properties of millimeter wave (mmWave) radar to achieve simultaneous tracking and sensing of multiple objects. This paper aims to provide a [...] Read more.

The boundaries of tracking and sensing solutions are continuously being pushed. A stimulation in this field over recent years is exploiting the properties of millimeter wave (mmWave) radar to achieve simultaneous tracking and sensing of multiple objects. This paper aims to provide a critical analysis of the current literature surrounding multi-object tracking and sensing with short-range mmWave radar. There is significant literature available regarding single-object tracking using mmWave radar, demonstrating the maturity of single-object tracking systems. However, innovative research and advancements are also needed in the field of mmWave radar multi-object tracking, specifically with respect to uniquely identifying multiple target tracks across an interrupted field of view. In this article, we aim to provide an overview of the latest progress in multi-target tracking. In particular, an attempt to phrase the problem space is made by firstly defining a typical multi-object tracking architecture. We then highlight the areas for potential advancements. These areas include sensor fusion, micro-Doppler feature analysis, specialized and generalized activity recognition, gait, tagging and shape profile. Potential multi-object tracking advancements are reviewed and compared with respect to adaptability, performance, accuracy and specificity. Although the majority of the literature reviewed has a focus on human targets, most of the methodologies can be applied to targets consisting of different profiles and characteristics to that of humans. Lastly, future research directions are also discussed to shed light on research opportunities and potential approaches in the open research areas. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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13 pages, 3384 KiB

Open AccessArticle

Zhongshan HF Radar Elevation Calibration Based on Ground Backscatter Echoes

by Weijie Jiang, Erxiao Liu, Xing Kong, Shengsheng Shi and Jianjun Liu

Electronics 2022, 11(24), 4236; https://doi.org/10.3390/electronics11244236 - 19 Dec 2022

Cited by 2 | Viewed by 1961

Abstract

The super dual auroral radar network (SuperDARN) is an important tool in the remote sensing of ionospheric potential convection in middle and high latitudes, and also a major source of elevation data detection. A reliable elevation angle helps estimate the propagation paths of [...] Read more.

The super dual auroral radar network (SuperDARN) is an important tool in the remote sensing of ionospheric potential convection in middle and high latitudes, and also a major source of elevation data detection. A reliable elevation angle helps estimate the propagation paths of high-frequency radio signals between scattering spots and radars, which is crucial for determining high-frequency radar target geolocation. The SuperDARN radar uses interferometry to estimate the elevation of the returned signal. However, elevation data are still underutilized owing to the difficulties of phase difference calibration induced by the propagation time delay between two arrays. This paper statistically analyzes the distribution features of the group range-elevation angle and group range-virtual height before and after calibration using elevation data from the ground backscatter echoes of the Zhongshan SuperDARN radar, calculating the root mean square error (RMSE) of the virtual height; the results show that the RMSE after calibration is mostly reduced to within 54% of that before calibration. Furthermore, we validate the calibration factor based on the primary phase data. The data from 2013 to 2015 indicate that this technique can be efficiently used to estimate the daily calibration factor. Finally, we present the statistical distribution of the calibration factor, which provides technical support for the calibration of elevation data in the future. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications)

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