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Electronics
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New Insights in Radar Signal Processing and Target Recognition
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New Insights in Radar Signal Processing and Target Recognition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1186

Special Issue Editors

Dr. Lingzhi Zhu

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: radar micro-Doppler effect; radar jamming and anti-jamming; radar measurement; radar target classification and recognition; digital signal processing; machine learning
Dr. Qi Zhang

E-Mail Website
Guest Editor
Faculty of Data Science, City University of Macau, Macau SAR 999078, China
Interests: machine learning; smart healthcare; pattern recognition; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Kuiyu Chen

E-Mail Website
Guest Editor
College of Electronic and Optical Engineering and the College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: radar signal processing; electronic countermeasure; radar electronic reconnaissance; deep learning

Special Issue Information

Dear Colleagues,

As wireless detection equipment, radar has been widely used in various fields, including autonomous driving, remote sensing, navigation, space target detection, target recognition and precision strike, due to its all-weather and all-day capacities, high distance resolution, etc. With the development of advanced signal processing methods and deep learning algorithms, radar has been applied in some new fields such as micro-Doppler target recognition, human health monitoring and smart security. But some new challenges are also gradually emerging, including but not limited to detection and recognition of small-sample targets in strong-clutter background and complex environments, respiration and heart rate monitoring of moving humans, pose-robustness representation extraction under different observation conditions, target recognition in space background, etc. Moreover, the continuous emergence of various types of radio equipment has also made radar face an increasingly complex electromagnetic environment in the civilian field. The development of new jammers in the military field greatly reduces the performance and survivability of radars. Signal reconnaissance, separation, identification, and anti-interference has been the study hotspot in the radar signal processing field.

This Special Issue aims to explore new insights in radar signal processing and target recognition. Original and novel contributions, including research papers and extensive reviews, addressing new radar signal processing methods, special radar application scenes, innovative radar target detection and recognition algorithms are welcomed. Research areas may include (but are not limited to) the following:

  • Radar micro-Doppler effect;
  • Synthetic aperture radar;
  • Inverse synthetic aperture radar;
  • Bi-static /multi-static radar target detection;
  • Distributed radar designing and imaging;
  • Space radar situational awareness;
  • Radar target detection and recognition in complex weathers;
  • Radar target detection and recognition in complex background environments;
  • Radar jamming and anti-jamming;
  • Radar signal reconnaissance, separation and recognition;
  • Radar signal parameter estimation;
  • Radar target modeling and verification;
  • Human health monitoring using radars;
  • Human posture recognition using radars;
  • Radar ID and security.

Dr. Lingzhi Zhu
Dr. Qi Zhang
Dr. Kuiyu Chen
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. Electronics 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 2400 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 target detection
  • radar target recognition
  • radar signal processing
  • radar jamming and anti-jamming
  • SAR and ISAR
  • distributed radars

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Published Papers (3 papers)

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22 pages, 24849 KiB  
Article
Blind Signal Separation with Deep Residual Networks for Robust Synthetic Aperture Radar Signal Processing in Interference Electromagnetic Environments
by Lixiong Fang, Jianwen Zhang, Yi Ran, Kuiyu Chen, Aimer Maidan, Lu Huan and Huyang Liao
Electronics 2025, 14(10), 1950; https://doi.org/10.3390/electronics14101950 - 11 May 2025
Viewed by 270
Abstract
With the rapid development of electronic technology, the electromagnetic interference encountered by airborne synthetic aperture radar (SAR) is no longer satisfied with a single type of interference, and it often encounters both suppressive and deceptive interference. In this manuscript, an algorithm based on [...] Read more.
With the rapid development of electronic technology, the electromagnetic interference encountered by airborne synthetic aperture radar (SAR) is no longer satisfied with a single type of interference, and it often encounters both suppressive and deceptive interference. In this manuscript, an algorithm based on blind signal separation (BSS) and deep residual learning is proposed for airborne SAR multi-electromagnetic interference suppression. Firstly, theoretical airborne SAR imaging in a multi-electromagnetic interference environment model is established, and the signal-mixed model of multi-electromagnetic interference is proposed. Then, a BSS algorithm using maximum kurtosis deconvolution and improved principal component analysis (PCA) is presented for suppressing the composite electromagnetic interference encountered by airborne SAR. Finally, in order to find the desired signal among multiple separated sources and to cope with the residual noise, a deep residual network is designed for signal recognition and denoising. This method uses a BSS algorithm with maximum kurtosis deconvolution and improved PCA to perform mixed signal separation. After performing signal separation, the original echo signal and the jamming can be obtained. To solve the separation order uncertainty and residual noise problems of the existing BSS algorithms, the deep residual network is designed to recognize airborne SAR signals after airborne SAR imaging. This algorithm has a better signal restoration degree, higher image restoration degree, and better compound interference suppression performance before and after anti-interference. Simulation and measurement results demonstrate the effectiveness of our presented algorithm. Full article
(This article belongs to the Special Issue New Insights in Radar Signal Processing and Target Recognition)
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24 pages, 8128 KiB  
Article
Model Adaptive Kalman Filter for Maneuvering Target Tracking Based on Variational Inference
by Junxiang Wang, Xin Wang, Yingying Chen, Mengting Yan and Hua Lan
Electronics 2025, 14(10), 1908; https://doi.org/10.3390/electronics14101908 - 8 May 2025
Viewed by 195
Abstract
This study introduces a new variational Bayesian adaptive estimator that enhances traditional interactive multiple model (IMM) frameworks for maneuvering target tracking. Conventional IMM algorithms struggle with rapid maneuvers due to model-switching delays and fixed structures. Our method uses Bayesian inference to update change-point [...] Read more.
This study introduces a new variational Bayesian adaptive estimator that enhances traditional interactive multiple model (IMM) frameworks for maneuvering target tracking. Conventional IMM algorithms struggle with rapid maneuvers due to model-switching delays and fixed structures. Our method uses Bayesian inference to update change-point statistics in real-time for quick model switching. Variational Bayesian inference approximates the complex posterior distribution, transforming target state estimation and model identification into an optimization task to maximize the evidence lower bound (ELBO). A closed-loop iterative mechanism jointly optimizes the target state and model posterior. Experiments in six simulated and two real-world scenarios show our method outperforms current algorithms, especially in high maneuverability contexts. Full article
(This article belongs to the Special Issue New Insights in Radar Signal Processing and Target Recognition)
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14 pages, 1244 KiB  
Article
Field-Programmable Gate Array Implementation of Backprojection Algorithm for Circular Synthetic Aperture Radar
by Jinmoo Heo, Seongjoo Lee and Yunho Jung
Electronics 2025, 14(8), 1544; https://doi.org/10.3390/electronics14081544 - 10 Apr 2025
Viewed by 227
Abstract
This paper presents a backprojection algorithm (BPA) accelerator implemented on a field-programmable gate array (FPGA) for circular synthetic aperture radar (SAR) systems. Although the BPA offers superior image quality, it requires significantly more computation and is memory intensive, necessitating hardware optimization. In particular, [...] Read more.
This paper presents a backprojection algorithm (BPA) accelerator implemented on a field-programmable gate array (FPGA) for circular synthetic aperture radar (SAR) systems. Although the BPA offers superior image quality, it requires significantly more computation and is memory intensive, necessitating hardware optimization. In particular, the BPA accumulates image data, leading to high memory requirements that must be reduced for embedded system implementation. To address this issue, we optimized the floating-point (FP) bit width, focusing on the output data that form the image, rather than only reducing the internal computation bit widths as in previous studies. Specifically, we optimized the exponent and mantissa widths in six computational units, prioritizing memory optimization for image data before reducing the computational logic. The proposed BPA accelerator achieved a 77% reduction in memory usage and a 73–74% reduction in computational logic while maintaining an image quality with a structural similarity index measure (SSIM) of 0.99 or higher. These optimizations significantly enhanced the feasibility of BPA processing in embedded systems. Full article
(This article belongs to the Special Issue New Insights in Radar Signal Processing and Target Recognition)
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