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Advances in Radar Systems for Target Detection and Tracking (Second Edition)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 7613

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Special Issue Editors

Dr. Xiaolong Li

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Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
Interests: radar target detection and tracking; signal detection and estimation

Dr. Chenguang Shi

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Guest Editor

College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Interests: radar resource-aware management and scheduling; radar waveform optimization as well as radar signal processing for target detection, tracking and localization
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Dr. Shisheng Guo

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Guest Editor

School of Information and Communication Engineering, University of Electronic Science and Technology of China(UESTC) , Chengdu 611731, China
Interests: radar-based target tracking; UWB radar target detection
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junkun Yan

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Guest Editor

National Laboratory of Radar Signal Processing, Xidian University, Xian 710071, China
Interests: radar signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radar systems allow the detection and tracking of targets of interest at any time and in all weathers and have been extensively applied in the remote-sensing community to applications such as geological exploration, disaster forecasting, traffic monitoring, urban planning, environmental sciences, hydrology, littoral zones, and oceans. Complicated target characteristics, complex environments, and refined processing requirements have presented great challenges in radar target detection, tracking, and recognition. Much work has been carried out with airborne, spaceborne, ground-based, and shore-based radar systems, and impressive progress has also been made in studying the methodology behind them. However, there is still a great deal of room for further research on radar target detection, tracking, and recognition. Therefore, it is necessary to summarize the recent research progress in radar systems for target detection and tracking.

This Special Issue aims to collect and highlight outstanding contributions that cover “Advances in Radar Systems for Target Detection and Tracking”, that can include (but are not limited to) the following topics:

Radar target detection, tracking, and imaging in ground/sea environments;
Radar target detection, tracking, and imaging in interference situations;
Joint radar sensor registration and target tracking in complex environments;
Radar resource management for target detection and tracking in complex environments;
Detection and tracking using SAR/InSAR images with applications in geology;
Combination of advanced signal processing and artificial intelligence techniques;
New radar systems, such as MIMO radar, distributed radar, dual multi-base radar, and so on;
Short-range radars, especially in the context of consumer (indoor environments) and automotive applications;
Deep learning-based target detection and tracking.

Dr. Xiaolong Li
Dr. Chenguang Shi
Dr. Shisheng Guo
Prof. Dr. Junkun Yan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

radar system design
target detection
target tracking
radar resource optimization and scheduling
signal and image processing
machine (deep) learning
environment monitoring (maritime, terrestrial, urban area, etc.)
synthetic aperture radar

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Advances in Radar Systems for Target Detection and Tracking in Remote Sensing (18 articles)

Published Papers (10 papers)

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Research

28 pages, 4219 KiB

Open AccessArticle

Angle Expansion Estimation and Correction Based on the Lindeberg–Feller Central Limit Theorem Under Multi-Pulse Integration

by Jiong Cai, Rui Wang and Handong Yang

Remote Sens. 2024, 16(23), 4535; https://doi.org/10.3390/rs16234535 - 3 Dec 2024

Viewed by 319

Abstract

The radar monopulse angle measurement can obtain a target’s angle information within a single pulse, meaning that factors such as target motion and amplitude fluctuations, which vary over time, do not affect the angle measurement accuracy. However, in practical applications, when a target’s signal-to-noise ratio (SNR) is low, the single pulse signal is severely affected by noise, leading to a significant deterioration in angle measurement accuracy. Therefore, it is usually necessary to coherently integrate multiple pulses before estimating the angle. This paper constructs an angle expansion model for a multi-pulse angle measurement under coherent integration. The analysis reveals that even under noise-free conditions, after coherently integrating multiple pulses, the coupling of target amplitude fluctuations and motion state can still cause significant errors in the angle measurement. Subsequently, this paper conducts a detailed analysis of the impact of the amplitude fluctuations and target maneuvers on the random angle measurement error. It also derives approximate probability density functions of angle measurement errors under various fluctuation and motion scenarios based on the Lindeberg–Feller central limit theorem. In addition, based on the angle expansion model and the random error distribution, this paper proposes an angle correction algorithm based on multi-pulse integration and long-term estimation. Numerical experiments and radar data in the field verify the impact of target characteristics on the angle measurement under multi-pulse integration and the effectiveness of the angle correction algorithm. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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17 pages, 2496 KiB

Open AccessArticle

Radar HRRP Feature Fusion Recognition Method Based on ConvLSTM Network with Multi-Input Gate Recurrent Unit

by Wei Yang, Tianqi Chen, Shiwen Lei, Zhiqin Zhao, Haoquan Hu and Jun Hu

Remote Sens. 2024, 16(23), 4533; https://doi.org/10.3390/rs16234533 - 3 Dec 2024

Viewed by 370

Abstract

Recently, the radar high-resolution range profiles (HRRPs) have gained significant attention in the field of radar automatic target recognition due to their advantages of being easy to acquire, having a small data footprint, and providing rich target structural information. However, existing recognition methods typically focus on single-domain features, utilizing either the raw HRRP sequence or the extracted feature sequence independently. To fully exploit the multi-domain information present in HRRP sequences, this paper proposes a novel target feature fusion recognition approach. By combining a convolutional long short-term memory (ConvLSTM) network with a cascaded gated recurrent unit (GRU) structure, the proposed method leverages multi-domain and temporal information to enhance recognition performance. Furthermore, a multi-input framework based on learnable parameters is designed to improve target representation capabilities. Experimental results of 6 ship targets demonstrate that the fusion recognition method achieves superior accuracy and faster convergence compared to methods relying on single-domain sequences. It is also found that the proposed method consistently outperforms the other previous methods. And the recognition accuracy is up to 93.32% and 82.15% for full polarization under the SNRs of 20 dB and 5 dB, respectively. Therefore, the proposed method consistently outperforms the previous methods overall. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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24 pages, 5848 KiB

Open AccessArticle

Clutter-Sensing-Driven Space-Time Adaptive Processing Approach for Airborne Sub-Array-Level Digital Array

by Youai Wu, Bo Jiu, Wenqiang Pu, Hao Zheng, Kang Li and Hongwei Liu

Remote Sens. 2024, 16(23), 4401; https://doi.org/10.3390/rs16234401 - 25 Nov 2024

Viewed by 388

Abstract

Sub-array-level digital arrays effectively diminish the computational complexity and sample demand of space-time adaptive processing (STAP), thus finding extensive applications in many airborne platforms. Nonetheless, airborne sub-array-level digital array radar still encounters pronounced performance deterioration in highly heterogeneous clutter environments due to inadequate training samples. To address this issue, a clutter-sensing-driven STAP approach for airborne sub-array-level digital arrays is proposed in this paper. Firstly, we derive a signal model of sub-array-level clutter sensing in detail and then further analyze the influence of the sidelobe characteristics of the conventional sub-array joint beam on clutter sensing. Secondly, a sub-array joint beam optimization model is proposed, which optimizes the sub-array joint beam into a wide beam with flat-top characteristics to improve the clutter-sensing performance in the beam sidelobe region. Finally, we decompose the complex optimization problem into two subproblems and then relax them into the low sidelobe-shaped beam pattern synthesisproblem and second-order cone programming problem, which can be effectively solved. The effectiveness of the proposed approach is validated in a real clutter environment through numerical experiments. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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20 pages, 5213 KiB

Open AccessArticle

Radar Moving Target Detection Based on Small-Sample Transfer Learning and Attention Mechanism

by Jiang Zhu, Cai Wen, Chongdi Duan, Weiwei Wang and Xiaochao Yang

Remote Sens. 2024, 16(22), 4325; https://doi.org/10.3390/rs16224325 - 20 Nov 2024

Viewed by 564

Abstract

Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior knowledge of the clutter distribution in the space–time domain, this paper proposes a novel moving target detection network based on small-sample transfer learning and attention mechanism. The proposed network first utilizes offline data to train the feature extraction network and reduce the online training time. Meanwhile, the attention mechanism used for feature extraction is applied in the beam-Doppler domain to improve classification accuracy of targets. Then, a small amount of real-time data are applied to a small-sample transfer network to fine-tune the feature extraction network. Finally, the target detection can be realized by the fine-tuned network. Simulation experiments show that the proposed network can eliminate the influence of heterogeneous clutter on moving target detection, and the attention mechanism can improve clutter suppression under a low signal-to-noise ratio regime. The proposed network has a lower computational load compared to conventional neural networks, enabling its use in real-time applications on space-borne/airborne radars. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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20 pages, 584 KiB

Open AccessArticle

Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

by Xiang Feng, Ping Sun, Lu Zhang, Guangle Jia, Jun Wang and Zhiquan Zhou

Remote Sens. 2024, 16(21), 3951; https://doi.org/10.3390/rs16213951 - 23 Oct 2024

Viewed by 828

Abstract

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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18 pages, 21647 KiB

Open AccessArticle

Modified Hybrid Integration Algorithm for Moving Weak Target in Dual-Function Radar and Communication System

by Wenshuai Ji, Tao Liu, Yuxiao Song, Haoran Yin, Biao Tian and Nannan Zhu

Remote Sens. 2024, 16(19), 3601; https://doi.org/10.3390/rs16193601 - 27 Sep 2024

Viewed by 726

Abstract

To detect moving weak targets in the dual function radar communication (DFRC) system of an orthogonal frequency division multiplexing (OFDM) waveform, a modified hybrid integration method is addressed in this paper. A high-speed aircraft can cause range walk (RW) and Doppler walk (DW), rendering traditional detection methods ineffective. To overcome RW and DW, this paper proposes an integration approach combining DFRC and OFDM. The proposed approach consists of two primary components: intra-frame coherent integration and hybrid multi-inter-frame integration. After the echo signal is re-fragmented into multiple subfragments, the first step involves integrating energy across fixed situations within intra-frames for each subcarrier. Subsequently, coherent integration is performed across the subfragments, followed by the application of a Radon transform (RT) to generate frames based on the properties derived from the coherent integration output. This paper provides detailed expressions and analyses for various performance metrics of our proposed method, including the communication bit error ratio (BER), responses of coherent and non-coherent outputs, and probability of detection. Simulation results demonstrate the effectiveness of our strategy. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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19 pages, 5104 KiB

Open AccessArticle

Mission Planning and Trajectory Optimization in UAV Swarm for Track Deception against Radar Network

by Yihan Li, Chenguang Shi, Mu Yan and Jianjiang Zhou

Remote Sens. 2024, 16(18), 3490; https://doi.org/10.3390/rs16183490 - 20 Sep 2024

Cited by 1 | Viewed by 739

Abstract

In this article, a mission planning and trajectory optimization scheme in unmanned aerial vehicle (UAV) swarm for track deception against radar networks is proposed. The core of this scheme is to formulate the track deception problem as a model with the objective of simultaneously maximizing the number of phantom tracks while minimizing the total flight distance of the UAV swarm, subject to the constraints of UAV kinematic performance, phantom track rotation angles, and a homology test. It is shown that the formulated track deception problem is a mixed-integer programming, multivariable, and non-linear optimization model. By incorporating mission planning based on platform reuse and a particle swarm optimization (PSO) algorithm, a three-stage solution methodology is proposed to tackle the above problem. Through joint optimization for mission planning and flight trajectories of the UAV swarm, a low-speed UAV swarm is capable of generating a number of high-speed phantom tracks. Numerical results demonstrate that the proposed scheme enables a low-speed UAV swarm to generate as many high-speed phantom tracks as possible, effectively achieving track deception against radar network. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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20 pages, 613 KiB

Open AccessArticle

Multi-Target Pairing Method Based on PM-ESPRIT-like DOA Estimation for T/R-R HFSWR

by Shujie Li, Xiaochuan Wu, Siming Chen, Weibo Deng and Xin Zhang

Remote Sens. 2024, 16(17), 3128; https://doi.org/10.3390/rs16173128 - 24 Aug 2024

Viewed by 918

Abstract

The transmit/receive-receive (T/R-R) synergetic High Frequency Surface Wave Radar (HFSWR) has increasingly attracted attention due to its high localization accuracy, but multi-target pairing needs to be performed before localization in multi-target scenarios. However, existing multi-target parameter matching methods have primarily focused on track association, which falls under the category of information-level fusion techniques, with few methods based on detected points. In this paper, we propose a multi-target pairing method with high computational efficiency based on angle information for T/R-R synergetic HFSWR. To be more specific, a dual-receiving array signal model under long baseline condition is firstly constructed. Then, the amplitude and phase differences of the same target reaching two subarrays are calculated to establish the cross-correlation matrix. Subsequently, in order to extract the rotation factor matrices containing pairing information and improve angle estimation performance, we utilize the conjugate symmetry properties of the uniform linear array (ULA) manifold matrix for generalized virtual aperture extension. Ultimately, azimuths estimation and multi-target pairing are accomplished by combining the propagator method (PM) and the ESPRIT algorithm. The proposed method relies solely on angle information for multi-target pairing and leverages the rotational invariance property of Vandermonde matrices to avoid peak searching or iterations, making it computationally efficient. Furthermore, the proposed method maintains superb performance regardless of whether the spatial angles are widely separated or very close. Simulation results validate the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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20 pages, 579 KiB

Open AccessArticle

2D DOA and Polarization Estimation Using Parallel Synthetic Coprime Array of Non-Collocated EMVSs

by Yunlong Yang, Mengru Shan and Guojun Jiang

Remote Sens. 2024, 16(16), 3004; https://doi.org/10.3390/rs16163004 - 16 Aug 2024

Viewed by 714

Abstract

For target detection and recognition in a complicated electromagnetic environment, the two-dimensional direction-of-arrival and polarization estimation using a polarization-sensitive array has been receiving increased attention. To efficiently improve the performance of such multi-parameter estimation in practice, this paper proposes a parallel synthetic coprime array with reduced mutual coupling and hardware cost saving and then presents a dimension-reduction compressive sensing-based estimation method. For the proposed array, the polarization types, numbers, and positions of antennas in each subarray are jointly considered to effectively mitigate mutual coupling in the physical array domain and to both enhance degrees of freedom and extend the aperture in the difference coarray domain with the limited physical antennas. By exploring the array configuration, the parameter estimation can be formulated as a block-sparse signal reconstruction problem, and then the one-dimensional sparse reconstruction algorithm is only used once to achieve multi-parameter estimation with automatic pair-matching. The theoretical analysis and simulation results are provided to demonstrate the superior performance of the proposed array and method over the existing techniques. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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24 pages, 1921 KiB

Open AccessArticle

Perturbation Transmit Beamformer Based Fast Constant Modulus MIMO Radar Waveform Design

by Hao Zheng, Hao Wu, Yinghui Zhang, Junkun Yan, Jian Xu and Yantao Sun

Remote Sens. 2024, 16(16), 2950; https://doi.org/10.3390/rs16162950 - 12 Aug 2024

Viewed by 1063

Abstract

In this paper, a fast method to generate a constant-modulus (CM) waveform for a multiple-input, multiple-output, (MIMO) radar is proposed. To simplify the optimization process, the design of the transmit waveform is decoupled from the design of transmit beamformers (TBs) and subpulses. To further improve the computational efficiency, the TBs’ optimization is conducted in parallel, and a linear programming model is proposed to match the desired beampattern. Additionally, we incorporate the perturbation vectors into the TBs’ optimization so that the TBs can be adjusted to satisfy the CM constraint. To quickly generate the CM subpulses with the desired range-compression (RC) performance, the classical linear frequency modulation (LFM) signal and non-LFM (NLFM) are adopted as subpulses. Meanwhile, to guarantee the RC performance of the final angular waveform, the selection of LFM signal parameters is analyzed to achieve a low cross-correlation between subpulses. Numerical simulations verify the transmit beampattern performance, RC performance, and computational efficiency of the proposed method. Full article

(This article belongs to the Special Issue Advances in Radar Systems for Target Detection and Tracking (Second Edition))

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Clutter-sensing Driven STAP Approach for Airborne Subarray-level Digital Array
Authors: Youai Wu; Bo Jiu; Wenqiang Pu; Hao Zheng; Kang Li; Hongwei Liu
Affiliation: Xidian University
Abstract: The subarray-level digital array effectively diminishes computational complexity and sample demand of space-time adaptive processing (STAP), thus finding extensive application in many airborne platforms. Nonetheless, airborne subarray-level digital array radar still encounters pronounced performance deterioration in highly heterogeneous clutter environments due to inadequate training samples. To address this issue, a clutter-sensing driven STAP approach for the airborne subarray-level digital array is proposed in this paper. Firstly, we derive the signal model of subarray-level clutter sensing in detail and then further analyze the influence of sidelobe characteristics of the conventional sub-array joint beam on clutter sensing. Secondly, a sub-array joint beam optimization model is proposed, which optimizes the sub-array joint beam into a wide beam with flat-top characteristics to improve the clutter sensing performance in the beam sidelobe region. Finally, we decompose the complex optimization problem into two subproblems and then relax them into the low sidelobe shaped-beam pattern synthesis problem and second-order cone programming problem, which can be effectively solved. The effectiveness of the proposed approach is validated in a real clutter environment through numerical experiments.

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