Radar Signal and Data Processing with Applications, 2nd Edition

Abstract submission deadline

31 August 2025

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30 November 2025

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Topic Information

Dear Colleagues,

Radars perform a significant role in the airborne, vehicle, shipborne, or surface deformation monitoring fields because of their all-day and all-weather abilities. Many studies have been carried out to improve the sensing precision of radars, such as multi-dimensional sensing, multi-domain detection, and super-resolution methods, among others. However, it is difficult to fully extract information using traditional data processing approaches. With the development of artificial intelligence, radar performance has been improved. Therefore, there is a need to further explore new AI technologies or advanced algorithms with high precision and performance. This Topic collection is open to researchers and authors who want to submit works in the fields of radar applications, new methods in radar signal processing, novel approaches to improving radar performance, and AI methods in radars. We are looking forward to submissions on topics of interest including but not limited to the following:

• Radar signal processing;
• AI in radar;
• Radar imaging;
• Super-resolution radar;
• Airborne radar;
• Vehicle radar;
• Shipborne radar;
• Surface deformation radar;
• Biomedical radar to include vital sign monitoring and other biomedical radar applications;
• Antennas for radar applications;
• mmWave radars;
• New radar applications.

Prof. Dr. Yin Zhang
Prof. Dr. Yulin Huang
Dr. Deqing Mao
Dr. Yanki Aslan
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.5	2011	19.8 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit
Sensors sensors	3.5	8.2	2001	19.7 Days	CHF 2600	Submit
Signals signals	2.6	4.6	2020	22.9 Days	CHF 1200	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit

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Published Papers (10 papers)

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20 pages, 7088 KiB  

Open AccessArticle

SAR Images Despeckling Using Subaperture Decomposition and Non-Local Low-Rank Tensor Approximation

by Xinwei An, Hongcheng Zeng, Zhaohong Li, Wei Yang, Wei Xiong, Yamin Wang and Yanfang Liu

Remote Sens. 2025, 17(15), 2716; https://doi.org/10.3390/rs17152716 - 6 Aug 2025

Viewed by 204

Abstract

Synthetic aperture radar (SAR) images suffer from speckle noise due to their imaging mechanism, which deteriorates image interpretability and hinders subsequent tasks like target detection and recognition. Traditional denoising methods fall short of the demands for high-quality SAR image processing, and deep learning [...] Read more.

Synthetic aperture radar (SAR) images suffer from speckle noise due to their imaging mechanism, which deteriorates image interpretability and hinders subsequent tasks like target detection and recognition. Traditional denoising methods fall short of the demands for high-quality SAR image processing, and deep learning approaches trained on synthetic datasets exhibit poor generalization because noise-free real SAR images are unattainable. To solve this problem and improve the quality of SAR images, a speckle noise suppression method based on subaperture decomposition and non-local low-rank tensor approximation is proposed. Subaperture decomposition yields azimuth-frame subimages with high global structural similarity, which are modeled as low-rank and formed into a 3D tensor. The tensor is decomposed to derive a low-dimensional orthogonal basis and low-rank representation, followed by non-local denoising and iterative regularization in the low-rank subspace for data reconstruction. Experiments on simulated and real SAR images demonstrate that the proposed method outperforms state-of-the-art techniques in speckle suppression, significantly improving SAR image quality. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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19 pages, 7674 KiB  

Open AccessArticle

Development of Low-Cost Single-Chip Automotive 4D Millimeter-Wave Radar

by Yongjun Cai, Jie Bai, Hui-Liang Shen, Libo Huang, Bing Rao and Haiyang Wang

Sensors 2025, 25(15), 4640; https://doi.org/10.3390/s25154640 - 26 Jul 2025

Viewed by 503

Abstract

Traditional 3D millimeter-wave radars lack target height information, leading to identification failures in complex scenarios. Upgrading to 4D millimeter-wave radars enables four-dimensional information perception, enhancing obstacle detection and improving the safety of autonomous driving. Given the high cost-sensitivity of in-vehicle radar systems, single-chip [...] Read more.

Traditional 3D millimeter-wave radars lack target height information, leading to identification failures in complex scenarios. Upgrading to 4D millimeter-wave radars enables four-dimensional information perception, enhancing obstacle detection and improving the safety of autonomous driving. Given the high cost-sensitivity of in-vehicle radar systems, single-chip 4D millimeter-wave radar solutions, despite technical challenges in imaging, are of great research value. This study focuses on developing single-chip 4D automotive millimeter-wave radar, covering system architecture design, antenna optimization, signal processing algorithm creation, and performance validation. The maximum measurement error is approximately ±0.2° for azimuth angles within the range of ±30° and around ±0.4° for elevation angles within the range of ±13°. Extensive road testing has demonstrated that the designed radar is capable of reliably measuring dynamic targets such as vehicles, pedestrians, and bicycles, while also accurately detecting static infrastructure like overpasses and traffic signs. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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25 pages, 19515 KiB  

Open AccessArticle

Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design

by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan and Zhiqi Gao

Remote Sens. 2025, 17(15), 2588; https://doi.org/10.3390/rs17152588 - 24 Jul 2025

Viewed by 397

Abstract

Synthetic Aperture Radar (SAR) provides all-weather, all-time imaging capabilities, enabling reliable maritime ship detection under challenging weather and lighting conditions. However, most high-precision detection models rely on complex architectures and large-scale parameters, limiting their applicability to resource-constrained platforms such as satellite-based systems, where [...] Read more.

Synthetic Aperture Radar (SAR) provides all-weather, all-time imaging capabilities, enabling reliable maritime ship detection under challenging weather and lighting conditions. However, most high-precision detection models rely on complex architectures and large-scale parameters, limiting their applicability to resource-constrained platforms such as satellite-based systems, where model size, computational load, and power consumption are tightly restricted. Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. Firstly, the backbone network integrates depthwise separable convolutions and a Convolutional Block Attention Module (CBAM) to suppress background clutter and extract effective features. Building upon this, a cross-layer feature interaction mechanism is introduced via the Multi-Scale Coordinated Fusion (MSCF) and Bi-EMA Enhanced Fusion (Bi-EF) modules to strengthen joint spatial-channel perception. To further enhance the detection capability, Efficient Feature Learning (EFL) modules are embedded in the neck to improve feature representation. Experiments on the Synthetic Aperture Radar (SAR) Ship Detection Dataset (SSDD) show that this method, with only 1.6 M parameters, achieves a mean average precision (mAP) of 98.35% in complex scenarios, including inshore and offshore environments. It balances the difficult problem of being unable to simultaneously consider accuracy and hardware resource requirements in traditional methods, providing a new technical path for real-time SAR ship detection on satellite platforms. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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27 pages, 18863 KiB  

Open AccessArticle

Angular Super-Resolution of Forward-Looking Scanning Radar via Grid-Updating Split SPICE-TV

by Ruitao Li, Jiawei Luo, Yin Zhang, Yongchao Zhang, Lu Jiao, Deqing Mao, Yulin Huang and Jianyu Yang

Remote Sens. 2025, 17(14), 2533; https://doi.org/10.3390/rs17142533 - 21 Jul 2025

Viewed by 250

Abstract

The sparse iterative covariance-based estimation (SPICE) method has recently gained significant attraction in the field of scanning radar super-resolution imaging because of its angular resolution enhancement capability. However, it is unable to preserve the target profile, and the estimator is constrained by high [...] Read more.

The sparse iterative covariance-based estimation (SPICE) method has recently gained significant attraction in the field of scanning radar super-resolution imaging because of its angular resolution enhancement capability. However, it is unable to preserve the target profile, and the estimator is constrained by high computational complexity and memory consumption. In this paper, a grid-updating split SPICE-TV algorithm is presented. The method allows for the efficient updating of reconstruction results with both contour and resolution, and a recursive grid-updating implementation framework of the split SPICE-TV has the capability to reduce the computational complexity. First, the scanning radar angular super-resolution problem is transformed into a constrained optimization problem by simultaneously employing sparse covariance fitting criteria and TV regularization constraints. Then, the split Bregman method is employed to derive an efficient closed-form solution to the problem. Ultimately, the matrix inversion problem is transformed into an online iterative equation to reduce the computational complexity and memory consumption. The superiority of the proposed method is verified by simulation and experimental data. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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27 pages, 3406 KiB  

Open AccessArticle

MSJosSAR Configuration Optimization and Scattering Mechanism Classification Based on Multi-Dimensional Features of Attribute Scattering Centers

by Shuo Liu, Fubo Zhang, Longyong Chen, Minan Shi, Tao Jiang and Yuhui Lei

Remote Sens. 2025, 17(14), 2515; https://doi.org/10.3390/rs17142515 - 19 Jul 2025

Viewed by 212

Abstract

As a novel system, multi-dimensional space joint-observation SAR (MSJosSAR) can simultaneously acquire target information across multiple dimensions such as frequency, angle, and polarization. This capability facilitates a more comprehensive understanding of the target and enhances subsequent recognition applications. However, current research on the [...] Read more.

As a novel system, multi-dimensional space joint-observation SAR (MSJosSAR) can simultaneously acquire target information across multiple dimensions such as frequency, angle, and polarization. This capability facilitates a more comprehensive understanding of the target and enhances subsequent recognition applications. However, current research on the configuration optimization of multi-dimensional SAR systems is limited, particularly in balancing recognition requirements with observation costs. This limitation has become a major bottleneck restricting the development of MSJosSAR. Moreover, studies on the joint utilization of multi-dimensional information at the scattering center level remain insufficient, which constrains the effectiveness of target component recognition. To address these challenges, this paper proposes a configuration optimization method for MSJosSAR based on the separability of scattering mechanisms. The approach transforms the configuration optimization problem into a vector separability problem commonly addressed in machine learning. Experimental results demonstrate that the multi-dimensional configuration obtained by this method significantly improves the classification accuracy of scattering mechanisms. Additionally, we propose a feature extraction and classification method for scattering centers across frequency and angle-polarization dimensions, and validate its effectiveness through electromagnetic simulation experiments. This study offers valuable insights and references for MSJosSAR configuration optimization and joint feature information processing. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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15 pages, 587 KiB  

Open AccessArticle

Research on a Particle Filtering Multi-Target Tracking Algorithm for Distributed Systems

by Bing Han, Zilong Ge, Zhigang Su and Jingtang Hao

Sensors 2025, 25(11), 3495; https://doi.org/10.3390/s25113495 - 31 May 2025

Viewed by 496

Abstract

The growth of unmanned aerial vehicle applications in the low-altitude economy demand advanced multi-target tracking systems. Unlike traditional approaches that assume independent measurements, distributed systems generate coupled measurements containing additional target relationship information. This paper proposes a novel distributed particle filtering algorithm through [...] Read more.

The growth of unmanned aerial vehicle applications in the low-altitude economy demand advanced multi-target tracking systems. Unlike traditional approaches that assume independent measurements, distributed systems generate coupled measurements containing additional target relationship information. This paper proposes a novel distributed particle filtering algorithm through introducing the coupled measurement into the conventional particle filtering method. In the proposed method, we fuse direct and coupled measurements via optimization and then build a cost function to optimize the particle weights. Comparative evaluations across motion models, noise levels, and the number of targets demonstrate the outperforming performance of the proposed method compared to conventional particle filtering and the unscented Kalman filtering algorithm, with more than 7% accuracy improvement over baselines. The results prove particular robustness to measurement noise and the increasing number of targets. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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22 pages, 6721 KiB  

Open AccessArticle

Online Sparse Reconstruction for Real Aperture Radar by Beam Recursive-Sliding Updating Framework

by Xichen Yin, Deqing Mao, Yongchao Zhang, Yin Zhang, Yulin Huang, Jianyu Yang and Qiping Zhang

Remote Sens. 2025, 17(11), 1887; https://doi.org/10.3390/rs17111887 - 29 May 2025

Viewed by 320

Abstract

Real aperture radar (RAR) can acquire the forward-looking target scene of interest continuously in scanning mode by arbitrary imaging geometry; however, the achievable angular resolution is predominantly governed by the physical dimensions of the antenna’s aperture. In contemporary radar imaging methodologies, the reconstruction [...] Read more.

Real aperture radar (RAR) can acquire the forward-looking target scene of interest continuously in scanning mode by arbitrary imaging geometry; however, the achievable angular resolution is predominantly governed by the physical dimensions of the antenna’s aperture. In contemporary radar imaging methodologies, the reconstruction of sparsely distributed targets can be effectively formulated as an $L_1$-regularized optimization framework through the exploitation of a priori sparsity constraints, thereby enabling the generation of enhanced-resolution forward-looking radar imagery. Nevertheless, traditional target reconstruction methods based on the sparse regularization framework are implemented after batch data collection, which comes at the cost of significant operational complexity and storage space. To address this challenge, an online sparse reconstruction method based on a beam recursive-sliding (BRS) updating framework is proposed to achieve fast target reconstruction. First, the antenna measurement matrix is repaired to reduce the imaging edge information error. Then, due to the independence of the echo data within two beamwidths, a beam recursive updating method is proposed for each two beamwidths echo data by the structural properties of the repaired antenna measurement matrix. Finally, based on the proposed beam recursive updating method, a sliding updating approach is proposed for the whole imaging region to reduce the computational redundancy and storage requirement. Simulation and experimental data demonstrate the effectiveness of the proposed BRS updating framework. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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25 pages, 11422 KiB  

Open AccessArticle

ESCI: An End-to-End Spatiotemporal Correlation Integration Framework for Low-Observable Extended UAV Tracking with Cascade MIMO Radar Subject to Mixed Interferences

by Guanzheng Hu, Xin Fang, Darong Huang and Zhenyuan Zhang

Electronics 2025, 14(11), 2181; https://doi.org/10.3390/electronics14112181 - 27 May 2025

Viewed by 440

Abstract

Continuous and robust trajectory tracking of unmanned aerial vehicles (UAVs) plays a crucial role in urban air transportation systems. Accordingly, this article presents an end-to-end spatiotemporal correlation integration (ESCI)-based UAV tracking framework by leveraging a high-resolution cascade multiple input multiple output (MIMO) radar. [...] Read more.

Continuous and robust trajectory tracking of unmanned aerial vehicles (UAVs) plays a crucial role in urban air transportation systems. Accordingly, this article presents an end-to-end spatiotemporal correlation integration (ESCI)-based UAV tracking framework by leveraging a high-resolution cascade multiple input multiple output (MIMO) radar. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this paper; the proposed method handles them jointly by integrating a continuous detection process into tracking. It not only eliminates the threshold decision-making process to avoid the loss of weak target information, but also significantly reduces the interference from other co-channel radars and strong clutters by exploring the spatiotemporal correlations within a sequence of radar frames, thereby improving the detectability of weak targets. In addition, to accommodate the time-varying number and extended size of radar reflections, with the ellipse spatial probability distribution model, the extended UAV with multiple scattering sources can be treated as an entity to track, and the complex measurement-to-object association procedure can be avoided. Finally, with Texas Instruments AWR2243 (TI AWR2243) we can utilize a cascade frequency-modulated continuous wave–multiple input multiple output (FMCW-MIMO) radar platform. The results show that the proposed method can obtain outstanding anti-interference performance for extended UAV tracking compared with state-of-the-art methods. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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24 pages, 9651 KiB  

Open AccessArticle

Three-Dimensional Localization Method of Underground Target Based on Miniaturized Single-Frequency Acoustically Actuated Antenna Array

by Chaowen Ju, Yixuan Liu, Jianle Liu, Tianxiang Nan, Xinger Cheng and Zhuo Zhang

Electronics 2025, 14(9), 1859; https://doi.org/10.3390/electronics14091859 - 2 May 2025

Viewed by 440

Abstract

The acoustically actuated antenna technology enables a significant reduction in antenna dimension, facilitating miniaturization of ground-penetrating radar systems in the very high-frequency (VHF) band. However, the current acoustically actuated antennas suffer from narrow bandwidth and low range resolution. To address this issue, this [...] Read more.

The acoustically actuated antenna technology enables a significant reduction in antenna dimension, facilitating miniaturization of ground-penetrating radar systems in the very high-frequency (VHF) band. However, the current acoustically actuated antennas suffer from narrow bandwidth and low range resolution. To address this issue, this paper proposed a three-dimensional (3D) localization method for underground targets, which combined two-dimensional (2D) array direction-of-arrival (DOA) estimation with continuous spatial sampling without relying on range resolution. By leveraging the small dimension of acoustically actuated antennas, a 2D uniform linear array was formed to obtain the target’s angle using DOA estimation. Based on the variation pattern of 2D angles in continuous spatial sampling, the genetic algorithm was employed to estimate the 3D coordinates of underground targets. The numerical simulation results indicated that the root mean square error (RMSE) of the proposed 3D localization method is 1.68 cm, which outperforms conventional methods that utilize wideband frequency-modulated pulse signals with hyperbolic vertex detection in theoretical localization accuracy, while also demonstrating good robustness. The gprMax electromagnetic simulation results further confirmed that this method can effectively localize multiple targets in ideal homogeneous underground media. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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16 pages, 746 KiB  

Open AccessArticle

A Multi-Receiver Pulse Deinterleaving Method Based on SSC-DBSCAN and TDOA Mapping

by Jie Xue, Binbin Su, Yongcai Liu and Jin Meng

Electronics 2025, 14(9), 1833; https://doi.org/10.3390/electronics14091833 - 29 Apr 2025

Cited by 1 | Viewed by 510

Abstract

Deinterleaving pulses of various pulse repetition interval (PRI) modulation modes constitute a vital and challenging task for an electronic measures system (ESM). A deinterleaving method based on multi-receiver time-difference-of-arrival (TDOA) is proposed in this paper. Firstly, this paper theoretically analyzes the distribution feature [...] Read more.

Deinterleaving pulses of various pulse repetition interval (PRI) modulation modes constitute a vital and challenging task for an electronic measures system (ESM). A deinterleaving method based on multi-receiver time-difference-of-arrival (TDOA) is proposed in this paper. Firstly, this paper theoretically analyzes the distribution feature of TDOA, providing the basis of deinterleaving. Then, a SSC (Sorting Skipping Clustering)-DBSCAN algorithm is proposed to achieve TDOA clustering by pre-sorting and traversing key points, which reduces the computational complexity. The TDOA mapping algorithm is further proposed to separate pulses and eliminate Cross-Pulse TDOAs simultaneously based on a one-time clustering result, which can significantly decrease the false alarm rate while avoiding clustering TDOA repeatedly. Simulation results show that the proposed method is capable of deinterleaving pulses of various PRI modulation modes and the performance remains excellent under multiple parameter settings. The running time and the false alarm rate have been reduced by at least 66% and 17%, respectively, compared with the existing methods. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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