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Remote Sensing
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Multi-Dimensional Radar Sensing: Systems, Algorithms, and Applications (Second Edition)
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Multi-Dimensional Radar Sensing: Systems, Algorithms, and Applications (Second Edition)

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

Deadline for manuscript submissions: closed (15 May 2026) | Viewed by 3392

Editors

Dr. Mou Wang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: 3D SAR imaging; computational imaging; sparse signal processing; electronic countermeasure reconnaissance
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Shi

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: image processing; radar signal processing; remote sensing applications; multisensor fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Xianxiang Yu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: MIMO radar; waveform design; radar array signal processing; electronic countermeasure technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shunjun Wei

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: multi-dimensional radar sensing; radar imaging and applications; radar remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jiahui Chen

E-Mail Website
Guest Editor Assistant
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: non-line-of-sight (NLOS); target detection and tomographic imaging

Special Issue Information

Dear Colleagues,

Radar is an irreplaceable sensor in many civil applications. It is able to reveal the target information without being affected by challenging weather conditions. In recent decades, multi-dimensional radar sensing techniques have shown promising results by using multiple frequencies, polarizations, and channels. However, original and innovative contributions on exploring the system potential, improving the algorithm performance, and addressing the key challenges are still required. Recent evolutions in terms of computationally efficient algorithms for high-dimensional data, robust sensing systems with low complexity, and AI-aided radar sensing applications point out the main directions in multi-dimensional radar sensing. For instance, machine learning techniques dramatically advanced the state of the art for many applications by leveraging the nature of adaptive feature representations. Motivated by this, intelligent radar perception/sensing has attracted the attention of many academies, research institutes, and space agencies, but the powerful learning framework still remains to be further exploited in the area of multi-dimensional radar sensing. Compressed sensing has brought revolutionary breakthroughs for accurately reconstructing sparse signals. It demonstrates that the signal can be recovered with a sub-Nyquist sampling rate, providing it is sparse or compressible. This gives us a promising way to simplify the sensing systems. In addition, many of the latest technologies and new ideas are expected to be used in this field. With this Special Issue, we aim to compile advanced research outcomes which specifically address various multi-dimensional radar sensing problems in terms of systems, algorithms, and applications. Papers for discussing the major challenges, latest developments, and recent advances in this area are highly welcomed.

Potential topics include, but are not limited to, the following points:

  • Multi-dimensional Radar Sensing: Advances in systems and algorithms;
  • Novel applications on multi-dimensional radar sensing;
  • MIMO and multistatic/distributed radar systems, schemes, and data processing techniques;
  • Three-dimensional SAR imaging with artificial intelligence and machine learning-based approaches;
  • Three-dimensional object detection with advanced techniques;
  • Deformation monitoring, polarimetric SAR image classification;
  • Object reconstruction from multidimensional radar point clouds;
  • Advanced data visualization techniques of multi-dimensional radars;
  • Image processing and image fusion for multi-sensor data;
  • Simultaneous localization and mapping (SLAM) with multi-dimensional radars;
  • Millimeter wave radar, Terahertz radar, and LIDAR techniques;
  • Advances in radar system implementation including waveform design, hardware design;
  • Reviews, techniques, designs or demos addressing future radar developments;
  • Novel sensing or imaging techniques potential for multi-dimensional radar applications;
  • Non-Line-of-Sight detection and imaging;
  • Intelligent-assisted Through-wall Radar imaging and application.

Dr. Mou Wang
Dr. Jun Shi
Dr. Xianxiang Yu
Prof. Dr. Shunjun Wei
Guest Editors

Dr. Jiahui Chen
Guest Editor Assistant

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. 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

  • multi-dimensional radar sensing
  • imaging and sensing algorithms
  • object detection and recognition
  • 3-D image and point cloud processing
  • simultaneous localization and mapping
  • high-dimensional data visualization
  • multi-sensors image fusion

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

26 pages, 14998 KB  
Article
Scattering Center Prior-Guided Diffusion for Unknown-Azimuth SAR Image Generation
by Bingyu Han, Mou Wang, Shunjun Wei, Kun Chen, Zeyang Dai, Jin Li, Xiaowo Xu, Xiaoling Zhang, Zongyong Cui, Di Jiang, Yuanyuan Zhou and Pengcheng Gao
Remote Sens. 2026, 18(14), 2417; https://doi.org/10.3390/rs18142417 - 21 Jul 2026
Viewed by 372
Abstract
Generating synthetic aperture radar (SAR) images at unknown azimuth angles under limited sample conditions remains a challenging task, since target scattering characteristics vary significantly with observation angle and are difficult to model effectively using only image-domain priors. To address this issue, this paper [...] Read more.
Generating synthetic aperture radar (SAR) images at unknown azimuth angles under limited sample conditions remains a challenging task, since target scattering characteristics vary significantly with observation angle and are difficult to model effectively using only image-domain priors. To address this issue, this paper proposes a scattering center prior-guided conditional diffusion framework for unknown-azimuth SAR image generation. First, the Iterative Shrinkage–Thresholding Algorithm (ISTA) constrained by the Point Spread Function (PSF) is employed to extract dominant scattering centers from SAR images at known viewing angles, obtaining sparse and physically interpretable scattering center maps. Subsequently, the target category, azimuth angle, and scattering map are jointly used as conditional vector inputs to train the diffusion model. During the inference stage, scattering maps from known azimuth angles are fused to construct a scattering prior for the unknown azimuth, which is used to guide the generation of the corresponding SAR image. Experimental results under sparse angular sampling conditions demonstrate that, compared with the scattering-guided GAN baseline and the non-learning image-domain interpolation baseline, the proposed method better preserves dominant scattering structures and generates unknown-azimuth SAR images with clearer target contours, more stable strong scattering regions, and fewer local artifacts. In summary, introducing dominant scattering center priors into the conditional diffusion model provides effective physical constraints for SAR image generation and improves unseen-azimuth SAR image generation under the evaluated sparse angular sampling conditions. Full article
(This article belongs to the Special Issue Multi-Dimensional Radar Sensing: Systems, Algorithms, and Applications (Second Edition))
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25 pages, 62695 KB  
Article
Doppler–Kinematic Spatio-Temporal Graph Learning for Low-Slow-Small Target Recognition Using Multi-Dimensional Radar Observations
by Jia Liu, Xiaolong Chen, Ningyuan Su, Hongyong Wang, Xinghai Wang and Yong Wang
Remote Sens. 2026, 18(13), 2151; https://doi.org/10.3390/rs18132151 - 2 Jul 2026
Viewed by 472
Abstract
Low-slow-small (LSS) target recognition using multi-dimensional radar remains challenging due to weak signatures, similar kinematics, and overlapping short-term Doppler patterns. Digital-array radar provides continuous, complementary Doppler-spectrum and kinematic measurements; however, their heterogeneity in dimension, distribution, and physical meaning often makes direct fusion under-exploit [...] Read more.
Low-slow-small (LSS) target recognition using multi-dimensional radar remains challenging due to weak signatures, similar kinematics, and overlapping short-term Doppler patterns. Digital-array radar provides continuous, complementary Doppler-spectrum and kinematic measurements; however, their heterogeneity in dimension, distribution, and physical meaning often makes direct fusion under-exploit discriminative complementarity and inadequately model temporal track evolution. To address this, we propose a Doppler-Kinematic Spatio-Temporal Graph Learning framework named Dual-Stream Spatio-Temporal Cross-Attention Graph Convolutional Network (DS-STCAGCN) for LSS target recognition using multi-dimensional radar observations. The method separately encodes Doppler-spectrum and kinematic features to preserve their modality-specific characteristics, fuses them through bidirectional cross-attention, captures long-range temporal dependencies via self-attention, and aggregates local frame-to-frame correlations through graph convolution on a time-ordered observation graph. On the public L-band digital-array dataset LSS-DAUR-1.0, DS-STCAGCN achieves 99.73% mean accuracy and maintains 98.64% at 5 dB signal-to-noise ratio (SNR). On the passive-radar dataset LSS-PR-1.0, it reaches 99.86% mean accuracy, demonstrating strong cross-modal generalization. This work provides an effective spatio-temporal modelling framework for multi-dimensional radar sensing and robust LSS target recognition. Full article
(This article belongs to the Special Issue Multi-Dimensional Radar Sensing: Systems, Algorithms, and Applications (Second Edition))
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21 pages, 887 KB  
Article
Enhanced Mainlobe Jamming Suppression in Distributed Array Radar via Joint Optimization of Radar Positions and Subpulse Frequencies
by Weiming Pu, Kewei Feng, Xiaoping Wang, Zhennan Liang, Xinliang Chen and Quanhua Liu
Remote Sens. 2025, 17(14), 2423; https://doi.org/10.3390/rs17142423 - 12 Jul 2025
Viewed by 1598
Abstract
This study presents a joint optimization framework for radar positions and subpulse carrier frequencies to address mainlobe jamming suppression in a distributed array radar system with one main and multiple auxiliary radars. Accounting for gain and aperture differences between the main and auxiliary [...] Read more.
This study presents a joint optimization framework for radar positions and subpulse carrier frequencies to address mainlobe jamming suppression in a distributed array radar system with one main and multiple auxiliary radars. Accounting for gain and aperture differences between the main and auxiliary radars, the grating lobe effect on jamming suppression performance is analyzed. Unlike conventional sparse array design approaches, this work introduces an architecture leveraging subpulses at distinct carrier frequencies to enhance grating lobe suppression and jamming suppression. A specific joint optimization method for radar positions and subpulse frequencies is then established. With jamming suppression performance as the objective function, the method first maps the variations induced by a range of candidate frequencies onto a single representative frequency point. This mapping enables efficient optimization of radar positions across the designated frequency band. Subsequently, a sequential scheme selects specific carrier frequencies for the subpulses. In practical anti-jamming operations, the optimal frequency for the current scenario is determined by analyzing the suppression results from these subpulses. The main radar then transmits pulses at this optimal frequency, thereby reducing both system complexity and pulse accumulation difficulty. Simulation results demonstrate that the proposed method achieves a reduction of over 3 dB in grating lobe suppression compared to conventional sparse array design methods, while enhancing the output signal-to-jamming and noise ratio by nearly 3 dB after jamming suppression. Full article
(This article belongs to the Special Issue Multi-Dimensional Radar Sensing: Systems, Algorithms, and Applications (Second Edition))
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