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Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2485

Special Issue Editors

Dr. Qihua Wu

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Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: (inverse) synthetic aperture radar imaging; radar anti-jamming; radar waveform design
Dr. Xiaobin Liu

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Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: radar anti-jamming; radar waveform design; radar image transformation
Dr. Zhiming Xu

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Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: characteristics and recognition of polarimetric radar targets
Prof. Dr. Siyuan He

E-Mail Website
Guest Editor
School of Earth and Space Science and Technology, Wuhan University, Wuhan 430072, China
Interests: SAR/ISAR target feature modeling; computational electromagnetic; electromagnetic scattering

Special Issue Information

Dear Colleagues,

Imaging radars, including synthetic aperture radar (SAR) and inverse SAR (ISAR), play a significant role in remote sensing areas due to its all-day and all-weather imaging abilities. In recent years, imaging radar techniques have attracted great attention, especially signal processing, waveform design, anti-jamming, target feature extraction, and target recognition. Advances in the relevant aspects contribute greatly to the progress of microwave remote sensing techniques. Hence, it is of great necessity and significance to conduct this Special Issue entitled “Advances in imaging radar signal processing, target feature extraction and recognition”.

This Special Issue will include research covering recent advances related to imaging radar techniques in remote sensing. Topics may cover anything from signal processing, waveform design, and anti-jamming to target feature extraction, target recognition, and so on. Articles may address, but are not limited, to the following topics:

  • SAR/ISAR imaging techniques;
  • SAR/ISAR waveform design;
  • SAR/ISAR image feature transform;
  • SAR/ISAR anti-jamming;
  • SAR/ISAR target feature extraction;
  • SAR/ISAR target recognition;
  • Pol-SAR/ISAR information processing.

Submissions focused exclusively on radar signal processing and algorithms without demonstrated applications in remote sensing will not be accepted. Examples include the following:

  1. Pure theoretical signal processing/radar array algorithms: studies lacking integration with remote sensing applications;
  2. Studies focused on electronic engineering or hardware design, e.g., radar antennae and RF circuit improvements;
  3. Studies focused on non-remote-sensing radar applications, e.g., industrial inspection or medical imaging;
  4. Military-focused technical details.

Dr. Qihua Wu
Dr. Xiaobin Liu
Dr. Zhiming Xu
Prof. Dr. Siyuan He
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 250 words) can be sent to the Editorial Office for assessment.

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

  • synthetic aperture radar (SAR)
  • inverse synthetic aperture radar (ISAR)
  • radar imaging
  • radar waveform design
  • radar anti-jamming
  • target feature extraction
  • target recognition

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 4796 KB  
Article
Deep Bayesian Optimization of Sparse Aperture for Compressed Sensing 3D ISAR Imaging
by Zongkai Yang, Jingcheng Zhao, Mengyu Zhang, Changyu Lou and Xin Zhao
Remote Sens. 2025, 17(19), 3380; https://doi.org/10.3390/rs17193380 - 7 Oct 2025
Viewed by 590
Abstract
High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling [...] Read more.
High-resolution three-dimensional (3D) Inverse Synthetic Aperture Radar (ISAR) imaging is essential for the characterization of target scattering in various environments. The practical application of this technique is frequently impeded by the lengthy measurement time necessary for comprehensive data acquisition with turntable-based systems. Sub-sampling the aperture can decrease acquisition time; however, traditional reconstruction algorithms that utilize matched filtering exhibit significantly impaired imaging performance, often characterized by a high peak side-lobe ratio. A methodology is proposed that integrates compressed sensing(CS) theory with sparse-aperture optimization to achieve high-fidelity 3D imaging from sparsely sampled data. An optimized sparse sampling aperture is introduced to systematically balance the engineering requirement for efficient, continuous turntable motion with the low mutual coherence desired for the CS matrix. A deep Bayesian optimization framework was developed to automatically identify physically realizable optimal sampling trajectories, ensuring that the sensing matrix retains the necessary properties for accurate signal recovery. This method effectively addresses the high-sidelobe problem associated with traditional sparse techniques, significantly decreasing measurement duration while maintaining image quality. Quantitative experimental results indicate the method’s efficacy: the optimized sparse aperture decreases the number of angular sampling points by roughly 84% compared to a full acquisition, while reconstructing images with a high correlation coefficient of 0.98 to the fully sampled reference. The methodology provides an effective solution for rapid, high-performance 3D ISAR imaging, achieving an optimal balance between data acquisition efficiency and reconstruction fidelity. Full article
(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)
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24 pages, 14264 KB  
Article
Convex-Decomposition-Based Evaluation of SAR Scene Deception Jamming Oriented to Detection
by Hai Zhu, Sinong Quan, Shiqi Xing and Haoyu Zhang
Remote Sens. 2025, 17(18), 3178; https://doi.org/10.3390/rs17183178 - 13 Sep 2025
Viewed by 569
Abstract
The evaluation of synthetic aperture radar (SAR) jamming effectiveness is a primary means to measure the reliability of jamming effects, and it can provide important guidance for the selection of jamming strategies and application of jamming styles. To address problems in traditional evaluation [...] Read more.
The evaluation of synthetic aperture radar (SAR) jamming effectiveness is a primary means to measure the reliability of jamming effects, and it can provide important guidance for the selection of jamming strategies and application of jamming styles. To address problems in traditional evaluation methods for SAR scene deception jamming, namely the simple adoption of native feature parameters, incomprehensive integration for evaluation indicator design, and the inconsideration of resulting jamming detection effects, this paper proposes a SAR scene deceptive jamming evaluation method oriented to jamming detection. First, four profound feature parameters including the brightness change gradient, texture direction contrast degree, edge matching degree, and noise suppression difference index are extracted in terms of visual and non-visual manners, which accurately highlight the differences between jamming and the background. Subsequently, through nonlinear iterative optimization and loss function design, a comprehensive evaluation indicator, i.e., with a convex decomposition is proposed, which can effectively quantify the contribution of each feature parameter and distinguish the differences in jamming concealment under different scenes. Finally, based on the measured and simulated MiniSAR datasets of urban, mountainous, and other complex scenes, a mapping correlation between the SDD and jamming detection rate is established. The evaluation results show that when the SDD is less than 0.4, the jamming is undetectable; when the SDD is greater than 0.4, for every 0.1 increase in the SDD, the jamming detection rate decreases by approximately 0.1. This provides support for the quantification of jamming effects in terms of detection rate in real applications. Full article
(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)
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27 pages, 9566 KB  
Article
CSBBNet: A Specialized Detection Method for Corner Reflector Targets via a Cross-Shaped Bounding Box Network
by Wangshuo Tang, Yuexin Gao, Mengdao Xing, Min Xue, Huitao Liu and Guangcai Sun
Remote Sens. 2025, 17(16), 2760; https://doi.org/10.3390/rs17162760 - 8 Aug 2025
Viewed by 772
Abstract
In synthetic aperture radar (SAR) maritime target detection tasks, corner reflector targets (CRTs) and their arrays can easily interfere with the accurate detection of ship targets, significantly increasing the misdetection rate and false alarm rate of detectors. Current deep learning-based research on SAR [...] Read more.
In synthetic aperture radar (SAR) maritime target detection tasks, corner reflector targets (CRTs) and their arrays can easily interfere with the accurate detection of ship targets, significantly increasing the misdetection rate and false alarm rate of detectors. Current deep learning-based research on SAR maritime target detection primarily focuses on ship targets, while dedicated detection methods addressing corner reflector interference have not yet established a comprehensive research framework. There remains a lack of theoretical innovation in detection principles for such targets. To address these issues, utilizing the prior knowledge of cross-shaped structures exhibited by marine CRTs in SAR images, we propose an innovative cross-shaped bounding box (CSBB) annotation strategy and design a novel dedicated detection network CSBBNet. The proposed method is constructed through three innovative component modules, namely the cross-shaped spatial feature perception (CSSFP) module, the wavelet cross-shaped attention downsampling (WCSAD) module, and the cross-shaped attention detection head (CSAD-Head). Additionally, to ensure effective training, we propose a cross-shaped intersection over union (CS-IoU) loss function. Comparative experiments with state-of-the-art methods demonstrate that our approach exhibits efficient detection capabilities for CRTs. Ablation experiment results validate the effectiveness of the proposed component architectures. Full article
(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)
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