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Advances in Radar Signal Processing Technology and Its Application
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Advances in Radar Signal Processing Technology and Its Application

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 4113

Editors

Dr. Ye Yuan

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: radar signal processing; radar resource allocation; target tracking; integrated sensing and communications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Junkun Yan

E-Mail Website
Guest Editor
National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
Interests: adaptive signal processing; target tracking; radar resource allocation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chenguang Shi

E-Mail Website
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 and radar signal processing for target detection, tracking, and localization
Special Issues, Collections and Topics in MDPI journals
Dr. Xinyu Liu

E-Mail Website
Guest Editor
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
Interests: radar signal processing; waveform design; ECM technology; joint radar-communication systems
Dr. Peng Zhang

E-Mail Website
Guest Editor
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: target detection and tracking; radar resource management; reinforcement learning

Special Issue Information

Dear Colleagues,

Radar remains a fundamental sensing technology for modern society, supporting applications ranging from air defense, space exploration, and maritime surveillance to weather monitoring, autonomous driving, and medical diagnostics. The increasing complexity of operating environments, characterized by dense clutter, spectrum congestion, and intentional electronic interference, together with the growing demand for high-resolution, multi-functional, and adaptive radar systems, has created increasing challenges. One potential solution regarding these challenges relies on the further development of radar signal processing, which serves as the core enabler for enhanced detection, estimation, imaging, classification, and tracking capabilities.

This Special Issue aims to provide a timely forum for presenting innovative methodologies, new theoretical frameworks, and practical implementations in radar signal processing. The goal is to highlight recent progress while also exploring emerging trends that will shape the next generation of radar systems.

Topics of interest include, but are not limited to, the following:

  • Advanced detection, estimation, and classification algorithms under complex environments;
  • Clutter, interference, and jamming suppression methods;
  • Distributed, networked, and MIMO radar processing architectures;
  • Adaptive waveform design and low probability of intercept (LPI) strategies;
  • Machine learning and deep learning approaches in radar signal processing;
  • Target recognition and behavioral analysis;
  • Data fusion with heterogeneous sensors (e.g., infrared, EO/IR, communication equipment);
  • Electronic counter-countermeasure (ECCM) techniques;
  • Applications in defense, security, transportation, environmental monitoring, and healthcare.

Dr. Ye Yuan
Prof. Dr. Junkun Yan
Prof. Dr. Chenguang Shi
Dr. Xinyu Liu
Dr. Peng Zhang
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-anonymized 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 signal processing
  • target detection and estimation
  • clutter and jamming suppression
  • distributed and MIMO radar
  • adaptive waveform design
  • machine learning in radar
  • low probability of intercept (LPI)
  • sensor fusion
  • electronic counter-countermeasures (ECCM)
  • radar applications

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Published Papers (6 papers)

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Research

20 pages, 2250 KB  
Article
A Micro-Doppler Flash Detection Framework for Hovering UAV Detection
by Tianxing Zhang, Rui Sun and Ye Yuan
Electronics 2026, 15(13), 2812; https://doi.org/10.3390/electronics15132812 - 25 Jun 2026
Viewed by 407
Abstract
This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not [...] Read more.
This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not only due to the spectral overlap between hovering targets and clutter but also because of the visual disappearance of micro-Doppler features under heavy noise. The framework consists of three sequential modules. A prior-template orthogonal projection (PTOP) module suppresses clutter via a single-step orthogonal projection, preserving the micro-Doppler flash signature without distortion while approximately maintaining the Gaussian noise statistics required for subsequent detection. A flash power spectrum construction module then collapses the periodic blade flash energy onto a sharp spectral peak in a one-dimensional (1D) power spectrum via Gabor transform, power projection, and fast Fourier transform (FFT). A cell-averaging constant false alarm rate (CA-CFAR) detection module with an analytically derived threshold factor finally renders a reliable detection decision. Simulations under a signal-to-clutter ratio (SCR) of −21 dB and signal-to-noise ratio (SNR) of −23 dB confirm that the proposed framework achieves reliable detection even when the micro-Doppler flash signatures are visually obscured by residual noise in the time–frequency domain. Parametric SNR sweep curves and a two-dimensional (2D) SCR–SNR detection-probability heatmap under a non-stationary clutter model further quantify the practical performance boundaries of the framework. By transforming these concealed periodic features into a sharp spectral peak, the framework provides robust detection performance where conventional range-Doppler and moving target indication (MTI)-based methods both exhibit severe performance degradation. Full article
(This article belongs to the Special Issue Advances in Radar Signal Processing Technology and Its Application)
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33 pages, 2721 KB  
Article
High-Precision DOA Estimation for Cyclostationary Signals Using an Augmented Extended Coprime Array and Atomic Norm Minimization
by Jiahao Liu, Yiran Shi, Hongxi Zhao, Wenchao He, Haoran Wang and Hewei Sun
Electronics 2026, 15(12), 2617; https://doi.org/10.3390/electronics15122617 - 13 Jun 2026
Viewed by 313
Abstract
Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags [...] Read more.
Direction-of-arrival (DOA) estimation of cyclostationary signals is an important problem in array signal processing, especially in sensor-limited and underdetermined scenarios. Sparse arrays and cyclostationary statistics can improve virtual degrees of freedom and target selectivity, but incomplete difference coarray information caused by missing lags may degrade virtual covariance reconstruction and reduce the reliability of DOA estimation in closely spaced, coherent, and interference-contaminated environments. To address this issue, this paper proposes a cyclostationary DOA estimation method based on an augmented extended coprime array (AECA), SVT-based hole recovery, and weighted atomic norm minimization (ANM). The proposed method first constructs the cyclic correlation matrix at the target cyclic frequency and maps it into the AECA-based virtual coarray domain. Redundant lag observations are then aggregated, and an iterative hole recovery procedure is applied to obtain an initial structured virtual covariance matrix. On this basis, a weighted ANM-based covariance refinement model is introduced, where directly observed lags and SVT-recovered hole entries are assigned different confidence levels. The final DOA estimates are obtained using MUSIC on the refined virtual covariance matrix. Simulation results under the considered underdetermined, closely spaced, coherent-source, and interference-contaminated scenarios show that the proposed method achieves lower RMSE and clearer spectral responses than the selected baseline methods. Additional ablation, parameter sensitivity, cyclic frequency mismatch, non-Gaussian noise, and runtime analyses further clarify the contribution, robustness range, and computational cost of the proposed framework. Full article
(This article belongs to the Special Issue Advances in Radar Signal Processing Technology and Its Application)
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22 pages, 22343 KB  
Article
A Unified Framework for Radar Signal Sorting and Recognition
by Haoyang Cheng, Xiao Li, Qi Tian, Wei Han, Xiaoliang Zhang, Jing Liang and Zheng Yang
Electronics 2026, 15(12), 2610; https://doi.org/10.3390/electronics15122610 - 12 Jun 2026
Viewed by 637
Abstract
Radar signal sorting (RSS) and radar emitter recognition (RER) constitute foundational yet challenging operations in electronic reconnaissance, where RSS aims to accurately segregate interleaved radar pulse streams and RER aims to recognize their originating emitters. Existing methods typically address RSS and RER as [...] Read more.
Radar signal sorting (RSS) and radar emitter recognition (RER) constitute foundational yet challenging operations in electronic reconnaissance, where RSS aims to accurately segregate interleaved radar pulse streams and RER aims to recognize their originating emitters. Existing methods typically address RSS and RER as separate processes within a sequential streaming framework, which neglect the inherent interdependence and collaborative potential between them, thereby resulting in error accumulation and performance bottleneck. In this paper, we redefine the radar signal sorting and recognition (RSSR) problem from an integrated modeling perspective, decomposing it into three sub-problems, i.e., signal pattern detection, signal pattern extraction, and detection result integration. In order to effectively solve these problems, we propose a novel Unified Framework inspired by Object Detection (UFiOD). Firstly, an end-to-end neural network is constructed to simultaneously optimize the regression of signal temporal occurrence regions and the recognition of signal categories. Then, a template matching algorithm is designed to extract corresponding pulses from the regions based on the signal categories. Finally, an integration algorithm based on temporal correlation and direction of arrival (DOA) fuses the detection results to generate object-level sorting and recognition conclusions. We extensively validate the effectiveness of the proposed method on simulation datasets. It demonstrates robust performance under various interleaving scenarios, including the interleaving of homogeneous radar emitters. Notably, it exhibits impressive capability for handling unknown signals, further highlighting its practical utility. Full article
(This article belongs to the Special Issue Advances in Radar Signal Processing Technology and Its Application)
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34 pages, 12654 KB  
Article
A General Optimization Framework for Radar Multi-PRF Waveform Synthesis Based on Bezout’s Identity and Genetic Algorithm
by Hang Su, Liang Zhang and Cheng Zhao
Electronics 2026, 15(10), 2130; https://doi.org/10.3390/electronics15102130 - 15 May 2026
Viewed by 482
Abstract
To mitigate the structural amplification of random false alarms during multi-pulse repetition frequency (Multi-PRF) ambiguity resolution, this paper proposes a general waveform synthesis optimization framework based on Bezout’s Identity and Genetic Algorithm (Bezout-GA). By leveraging Bezout’s Theorem, the framework establishes an analytical mapping [...] Read more.
To mitigate the structural amplification of random false alarms during multi-pulse repetition frequency (Multi-PRF) ambiguity resolution, this paper proposes a general waveform synthesis optimization framework based on Bezout’s Identity and Genetic Algorithm (Bezout-GA). By leveraging Bezout’s Theorem, the framework establishes an analytical mapping between the Greatest Common Divisor (GCD) topology of transmission parameters and system-level false alarm boundaries. It is mathematically demonstrated that the uncontrolled inflation of the Least Common Multiple (LCM) in traditional coprime-based strategies leads to severe “spatial over-issuance” of false alarms, a phenomenon particularly exacerbated in heavy-tailed K-distributed sea clutter. The proposed two-stage hybrid paradigm employs a genetic algorithm for global multi-objective search, followed by local number-theoretic refinement via the Extended Euclidean Algorithm to strictly satisfy hardware constraints. Simulations across X-band and L-band scenarios confirm the framework’s superior spectral generalizability. Results indicate that the Bezout-GA optimized waveform achieves a 4.1-fold reduction in expected false alarm volume at the cost of a negligible 0.1% clear-region sacrifice. Notably, in extreme K-distributed clutter (ν=0.1), the framework reclaims an equivalent signal-to-clutter-and-noise ratio (SCNR) gain of up to 3 dB in the L-band, significantly outperforming traditional coprime and maximum clear-region benchmarks. Overall, this study provides a number-theoretic perspective for analyzing spatial false alarm mechanisms and serves as a methodological reference for future investigations into robust Multi-PRF waveform optimization. Full article
(This article belongs to the Special Issue Advances in Radar Signal Processing Technology and Its Application)
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24 pages, 26483 KB  
Article
E-STNet: A Non-Ideal Array DOA Estimation Method Based on Enhanced Spatio-Temporal Features
by Haiqin Zhao, Jian Gong and Changlin Zhou
Electronics 2026, 15(6), 1270; https://doi.org/10.3390/electronics15061270 - 18 Mar 2026
Viewed by 498
Abstract
To address the challenge of degraded DOA estimation performance under array errors and low signal-to-noise ratio conditions, this paper proposes an Enhanced Spatio-Temporal Network (E-STNet). This network adopts a dual-source input architecture. By integrating multi-scale pooling and a hybrid Long Short-Term Memory-Transformer (LSTM-Transformer) [...] Read more.
To address the challenge of degraded DOA estimation performance under array errors and low signal-to-noise ratio conditions, this paper proposes an Enhanced Spatio-Temporal Network (E-STNet). This network adopts a dual-source input architecture. By integrating multi-scale pooling and a hybrid Long Short-Term Memory-Transformer (LSTM-Transformer) encoder, the network jointly refines spatial feature representations and captures multi-granularity temporal dependencies. Simulation results demonstrate that, under challenging scenarios such as array errors, low Signal-to-Noise Ratio (SNR), and closely spaced sources, E-STNet achieves higher estimation accuracy and stronger robustness than conventional algorithms and existing deep learning methods, providing an effective solution for DOA estimation in complex environments. Full article
(This article belongs to the Special Issue Advances in Radar Signal Processing Technology and Its Application)
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17 pages, 3310 KB  
Article
Research on an Adaptive Selection Method for GNSS Signals in Passive Radar
by Hongwei Fu, Hao Cha, Yu Luo, Tingting Fu, Bin Tian and Huatao Tang
Electronics 2026, 15(3), 648; https://doi.org/10.3390/electronics15030648 - 2 Feb 2026
Viewed by 745
Abstract
Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes [...] Read more.
Limited computational resources prevent GNSS-based passive radar systems from processing all accessible signals, necessitating intelligent signal selection for efficient target tracking. This paper proposes an adaptive selection method based on Rényi divergence. Within the Cardinality Balanced Multi-Bernoulli (CBMeMBer) filter framework, the method establishes an optimization model that maximizes the expected information gain under a fixed signal-number constraint. To comprehensively validate performance, simulations are conducted under three scenarios: multi-target linear motion, single-target tracking (for comparison with the classical Geometric Dilution of Precision (GDOP) criterion), and multi-target nonlinear maneuvering. Results demonstrate that the proposed algorithm significantly reduces computational load while achieving tracking accuracy superior to random selection and comparable to using all satellites. Compared to the GDOP-based method, it exhibits improved steady-state tracking accuracy by leveraging its dynamic, information-driven selection mechanism. This work provides an effective solution for intelligent resource management in resource-constrained GNSS-based passive radar systems. Full article
(This article belongs to the Special Issue Advances in Radar Signal Processing Technology and Its Application)
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