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Radar High-Speed Target Detection, Tracking, Imaging and Recognition - 2nd Edition

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (21 September 2023) | Viewed by 11134

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Special Issue Editors

Prof. Dr. Jibin Zheng

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National Lab of Radar Signal Processing, Department of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: synthetic aperture radar (SAR); inverse SAR signal processing; cognitive radar; time-frequency analysis; FPGA IP design
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaolong Chen

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Department of Electronic Information Engineering, Naval Aviation University, Yantai 246000, China
Interests: radar signal processing; AI for radar target detection
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bo Chen

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National Lab of Radar Signal Processing, Department of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: machine learning; statistical signal processing; radar target recognition and detection; deep learning network; large-scale data processing
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Prof. Dr. Junjie Wu

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School of Information and Communication Engineering, University of Electronics Science and Technology of China, Chengdu 611731, China
Interests: radar imaging; target detection; array signal processing
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Ran

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Guest Editor

National Lab of Radar Signal Processing, Department of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: synthetic aperture radar (SAR); forwarding-looking imaging; radar positioning

Special Issue Information

Dear Colleagues,

Complicated target characteristics, complex environment and refined processing requirement have made great challenges to radar high-speed target detection, tracking and recognition. Much work has been done with the airborne, spaceborne, ground-based and shore-based radars, and great progresses has also been made in the methodology research. However, along with appearances of new high-speed targets, attack forms, exploration requirements and processing techniques, there is still much research room on radar high-speed target detection, tracking and recognition, such as the characteristics modeling, netted radar system, combination of advanced signal processing and artificial intelligence techniques, automotive radar, and so on. The special issue aims to collect and highlight outstanding contributions on recent state of-the-art techniques in this field. Submissions should address the following topics:

Radar high-speed target characteristics modeling and analysis
High-speed target feature extraction
High-speed target detection, tracking, imaging and recognition in clutter and interference
Combination of advanced signal processing and artificial intelligence techniques
New radar system, such as MIMO radar, distributed radar, dual multi-base radar, and so on.
Resource distribution
Radar coherent processing
Multi-sensor data fusion
Technique reviews on the related topics

Prof. Dr. Jibin Zheng
Dr. Xiaolong Chen
Prof. Dr. Bo Chen
Prof. Dr. Junjie Wu
Dr. Lei Ran
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.

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Published Papers (6 papers)

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Research

20 pages, 6971 KiB

Open AccessArticle

Space Targets with Micro-Motion Classification Using Complex-Valued GAN and Kinematically Sifted Methods

by Lixun Han, Cunqian Feng and Xiaowei Hu

Remote Sens. 2023, 15(21), 5085; https://doi.org/10.3390/rs15215085 - 24 Oct 2023

Cited by 2 | Viewed by 1115

Abstract

Space target classification based on micro-motion characteristics has become a subject of great interest in the field of radar, particularly when using deep learning techniques. However, in practical applications, the ability of deep learning is hampered by the available radar datasets. As a result, obtaining a sufficient amount of the training dataset is a daunting challenge. To address this issue, this paper presents a novel framework for space target classification, consisting of three distinct modules: dataset generation, the kinematically sifted module, and classification. Initially, the micro-motion model of cone-shaped space targets is constructed to analyze target characteristics. Subsequently, the dataset generation module employs a complex-valued generative adversarial network (CV-GAN) to generate a large number of time-range maps. These maps serve as the foundation for training the subsequent modules. Next, the kinematically sifted module is introduced to eliminate images that do not align with the micro-motion characteristics of space targets. By filtering out incompatible images, the module ensures that only relevant and accurate dataset is utilized for further analysis. Finally, the classification model is constructed using complex-valued parallel blocks (CV-PB) to extract valuable information from the target. Experimental results validate the effectiveness of the proposed framework in space micro-motion target classification. The main contribution of the framework is to generate a sufficient amount of high-quality training data that conforms to motion characteristics, and to achieve accurate classification of space targets based on their micro-motion signatures. This breakthrough has significant implications for various applications in space target classification. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition - 2nd Edition)

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23 pages, 1658 KiB

Open AccessArticle

An SAR Image Automatic Target Recognition Method Based on the Scattering Parameter Gaussian Mixture Model

by Jikai Qin, Zheng Liu, Lei Ran, Rong Xie, Junkui Tang and Hongyu Zhu

Remote Sens. 2023, 15(15), 3800; https://doi.org/10.3390/rs15153800 - 30 Jul 2023

Cited by 5 | Viewed by 1663

Abstract

General synthetic aperture radar (SAR) image automatic target recognition (ATR) methods perform well under standard operation conditions (SOCs). However, they are not effective in extended operation conditions (EOCs). To improve the robustness of the ATR system under various EOCs, an ATR method for SAR images based on the scattering parameter Gaussian mixture model (GMM) is proposed in this paper. First, an improved active contour model (ACM) is used for target–background segmentation, which is more robust against noise than the constant false alarm rate (CFAR) method. Then, as the extracted attributed scattering center (ASC) is sensitive to noise and resolution, the GMM is constructed using the extracted ASC set. Next, the weighted Gaussian quadratic form distance (WGQFD) is adopted to measure the similarity of GMMs for the recognition task, thereby avoiding false alarms and missed alarms caused by the varying number of scattering centers. Moreover, adaptive aspect–frame division is employed to reduce the number of templates and improve recognition efficiency. Finally, based on the public measured MSTAR dataset, different EOCs are constructed under noise, resolution change, model change, depression angle change, and occlusion of different proportions. The experimental results under different EOCs demonstrate that the proposed method exhibits excellent robustness while maintaining low computation time. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition - 2nd Edition)

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22 pages, 7225 KiB

Open AccessArticle

A Two-Stage Track-before-Detect Method for Non-Cooperative Bistatic Radar Based on Deep Learning

by Wei Xiong, Yuan Lu, Jie Song and Xiaolong Chen

Remote Sens. 2023, 15(15), 3757; https://doi.org/10.3390/rs15153757 - 28 Jul 2023

Cited by 2 | Viewed by 1745

Abstract

Compared with traditional active detection radar, non-cooperative bistatic radar has a series of advantages, such as a low cost and low detectability. However, in real-life scenarios, it is limited by the non-cooperation of the radiation source and the bistatic geometric model, resulting in a low target signal-to-noise ratio (SNR) and unstable detection between frames in the radar scanning cycle. The traditional detect-before-track (DBT) method fails to exploit adequately the target information and is incapable of achieving consistent and effective tracking. Therefore, in this paper, we propose a two-stage track-before-detect (TBD) method based on deep learning. This method employs a low-threshold detection network to identify the target initially, followed by utilizing the model method to ascertain potential tracks. Subsequently, a diverse range of network structures are employed to extract and integrate position information, innovation score, and target structural information from the track in order to obtain the target track. Experimental results demonstrate the method’s ability to achieve multi-target tracking in highly cluttered environments, where the higher the number of frames processed, the better the target tracking effect. Moreover, the method exhibits real-time processing capabilities. Hence, this method provides an effective solution for target tracking in non-cooperative bistatic radar systems. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition - 2nd Edition)

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20 pages, 5643 KiB

Open AccessArticle

Micromotion Feature Extraction with VEMW Radar Based on Rotational Doppler Effect

by Kun Lv, Hui Ma, Xinrui Jiang, Jian Bai and Hongwei Liu

Remote Sens. 2023, 15(11), 2847; https://doi.org/10.3390/rs15112847 - 30 May 2023

Cited by 1 | Viewed by 1420

Abstract

Micro-Doppler (m-D) analysis is the most effective mechanism for detecting rotating targets or components; however, it fails when the target rotation plane is perpendicular to the radar line of sight (LOS). The vortex electromagnetic wave (VEMW) provides a unconventional structure of wavefront phase modulation on the cross-plane of the radar LOS, on which the radial m-D vanishes while the rotational Doppler (RD) appears. In the absence of the position of rotation center, this paper focuses on the micromotion parameters estimation based on RD effect for rotating target, and then proposes an estimation procedure, referred to as the two-step method. The micromotion parameters of the rotating target include the rotation attitude, the rotation radius and the position of the rotation center while the latter is coupled to the former two. Firstly, the micromotion parameters are roughly estimated based on the RD curve parameters obtained from the time-frequency (TF) spectrum of the received signal. Secondly, the maximum likelihood estimation (MLE) is used to accurately estimate the micromotion parameters. In addition, the Cramér–Rao bound (CRB) of parameter estimation is derived. The simulation studies the influencing factors of estimation performance and verifies that the proposed estimation method can provide excellent estimation accuracy of the micromotion parameters. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition - 2nd Edition)

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31 pages, 4110 KiB

Open AccessArticle

Multi-Dimensional Spread Target Detection with Across Range-Doppler Unit Phenomenon Based on Generalized Radon-Fourier Transform

by Guanxing Wang, Yangkai Wei, Zegang Ding, Pengjie You, Siyuan Liu and Tianyi Zhang

Remote Sens. 2023, 15(8), 2158; https://doi.org/10.3390/rs15082158 - 19 Apr 2023

Cited by 1 | Viewed by 1536

Abstract

Severe phenomena of across range-Doppler unit (ARDU) and decoherence occur when radar detects high-speed and high-maneuvering targets, causing degradation in detection performance of traditional FFT radar detection methods. The improvement in radar resolution causes a multi-dimensional spread phenomenon, where different scattering centers of the target are distributed on different range units, along with motion parameters such as velocity and acceleration. Unfortunately, current radar detection methods focus solely on range spread targets and cannot handle multi-dimensional spread, leading to a significant decline in detection performance. To overcome this problem, this paper proposes several methods to achieve high detection performance for multi-dimensional spread target detection with ARDU phenomenon. Firstly, the generalized likelihood ratio test (GLRT) is derived, and the energy integration generalized Rayleigh Fourier transform (EI-GRFT) is introduced to improve the detection performance of range spread cross-unit targets. Additionally, the double-threshold based hybrid GRFT (DT-HGRFT) is presented as an enhancement of EI-GRFT, enabling long-time integration along slow time and integration among multiple scatters by using HGRFT and multi-dimensional sliding double-threshold detection, respectively. Furthermore, a method for joint detections of multiple DT-HGRFTs is provided to handle the case where the number of scattering centers of multi-dimensional spread targets is unknown. Finally, a detailed theoretical analysis of the performance of the proposed method is presented, along with extensive simulations and practical experiments to demonstrate the effectiveness of the proposed methods. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition - 2nd Edition)

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21 pages, 3369 KiB

Open AccessArticle

Forward-Looking Super-Resolution Imaging of MIMO Radar via Sparse and Double Low-Rank Constraints

by Junkui Tang, Zheng Liu, Lei Ran, Rong Xie and Jikai Qin

Remote Sens. 2023, 15(3), 609; https://doi.org/10.3390/rs15030609 - 19 Jan 2023

Cited by 1 | Viewed by 2567

Abstract

Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods. Full article

(This article belongs to the Special Issue Radar High-Speed Target Detection, Tracking, Imaging and Recognition - 2nd Edition)

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