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Remote Sensing
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Radar Data Processing and Analysis
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Radar Data Processing and Analysis

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3699

Special Issue Editors

Prof. Dr. Xingpeng Mao

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 15001, China
Interests: radar signal processing; passive radar; high frequency radar
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. Zhenhua Zhang

E-Mail Website
Guest Editor
School of Astronautics, Beihang University, Beijing 100191, China
Interests: radar signal processing; cooperative detection and guidance
Prof. Dr. Yongzhen Li

E-Mail Website
Guest Editor
State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: polarimetric radar; target recognition; electronic interference
Prof. Dr. Yun Zhang

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 15001, China
Interests: SAR moving target detection; video SAR target tracking; SAR intelligent algorithm

Special Issue Information

Dear Colleagues,

Radar technology has become an indispensable tool in modern remote sensing applications, offering unique capabilities for imaging, detection, and environmental monitoring. Advancements in radar system design, signal processing, and data analysis have facilitated groundbreaking applications in remote sensing, including geosciences, environmental studies, and civil engineering.

We are pleased to invite researchers and professionals to contribute to this Special Issue, which aims to explore cutting-edge developments in radar technology and its applications. This Special Issue aligns closely with the journal's scope, focusing on innovative radar techniques and their impact on remote sensing, environmental monitoring, and other critical fields. By addressing challenges in radar signal processing, system design, and data analysis, this collection seeks to advance the understanding and utility of radar technologies in remote sensing.

This Special Issue aims to highlight state-of-the-art developments in radar signal and data processing techniques and their applications, bringing together contributions from academia and industry. The Issue will highlight state-of-the-art solutions and applications, encouraging submissions that align with the journal's goals of presenting reproducible and impactful research.

This Special Issue welcomes original research articles and review papers on a wide range of topics related to radar data processing and analysis. Suggested themes include, but are not limited to, the following:

  1. Remote sensing using radar techniques;
  2. Advanced radar signal processing techniques;
  3. Intelligent radar data analysis using machine learning and AI;
  4. Polarimetric radar and radar polarization signal processing;
  5. Passive radar systems;
  6. Distributed radar systems and networks;
  7. SAR imaging and applications;
  8. MIMO radar processing;
  9. Target detection, tracking, and recognition algorithms;
  10. Radar interference and anti-jamming technologies.

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.

I/We look forward to receiving your contributions.

Prof. Dr. Xingpeng Mao
Prof. Dr. Junkun Yan
Prof. Dr. Zhenhua Zhang
Prof. Dr. Yongzhen Li
Prof. Dr. Yun 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 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. 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

  • radar signal processing
  • synthetic aperture radar (SAR)
  • distributed radar
  • passive radar
  • machine learning in radar
  • polarimetric radar
  • radar interference and anti-jamming
  • target detection and tracking
  • MIMO radar

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

Published Papers (5 papers)

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Research

19 pages, 17439 KB  
Article
Dual-Polarization Radar Deception Jamming Method Based on Joint Fast-Slow-Time Polarization Modulation
by Yongfei Zhang, Yong Yang, Chao Hu, Jingwen Han and Boyu Yang
Remote Sens. 2025, 17(17), 2952; https://doi.org/10.3390/rs17172952 - 25 Aug 2025
Viewed by 697
Abstract
To address the vulnerability of single-polarization deception jamming and simply modulated dual-polarization jamming to discrimination by dual-polarization radars, this paper proposes a deception jamming method based on joint fast–slow-time polarization modulation (FSPMJ). First, in the slow-time domain (across multiple pulses), the polarization azimuth [...] Read more.
To address the vulnerability of single-polarization deception jamming and simply modulated dual-polarization jamming to discrimination by dual-polarization radars, this paper proposes a deception jamming method based on joint fast–slow-time polarization modulation (FSPMJ). First, in the slow-time domain (across multiple pulses), the polarization azimuth of the jamming signal is designed according to the target’s polarization ratio distribution. Subsequently, with the target polarization degree as the optimization objective, the polarization phase difference of the jamming signal is solved using an interior-point optimization algorithm, establishing the initial polarization state for each pulse. This process is iterated to design the polarization state for the first half of each pulse. Then, in the fast-time domain (within a single pulse), a polarization state orthogonal to the pre-generated first-half state, is constructed to serve as the polarization state for the latter half of each pulse. Finally, the effectiveness of the proposed method is validated through combined simulation and measured data using a Support Vector Machine (SVM) algorithm. Results demonstrate that compared to single-polarization deception jamming and existing polarization-modulated jamming, this method reduces the false target discrimination rate of dual-polarization radars by 35.4% without requiring complex target scattering matrices. Full article
(This article belongs to the Special Issue Radar Data Processing and Analysis)
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28 pages, 9221 KB  
Article
Adaptive Grid Expected Model Augmentation Based on Golden Section for Maneuvering Extended Object Tracking
by Lifan Sun, Shuo Sun, Dongkai Zhang, Bo Fan and Dan Gao
Remote Sens. 2025, 17(16), 2832; https://doi.org/10.3390/rs17162832 - 14 Aug 2025
Viewed by 322
Abstract
Maneuvering extended object tracking has garnered significant attention owing to the continuous advancements in the resolution capabilities of modern high-precision radar sensors. The efficacy of tracking algorithms for such objects is heavily contingent upon the design of the model set. However, existing methodologies [...] Read more.
Maneuvering extended object tracking has garnered significant attention owing to the continuous advancements in the resolution capabilities of modern high-precision radar sensors. The efficacy of tracking algorithms for such objects is heavily contingent upon the design of the model set. However, existing methodologies for model set design often yield suboptimal performance when confronted with highly maneuvering extended objects. The expected model augmentation (EMA) algorithm offers a data-driven mechanism for updating the model set in real time. Despite its advantages, the EMA algorithm is constrained by the fixed parameters of its basic models and static transition probabilities between models, thereby limiting its adaptability to extended objects exhibiting complex and dynamic maneuvering behaviors. To address these limitations, this paper proposes a modified variable structure multiple model (VSMM) framework for maneuvering extended object tracking, referred to as the adaptive grid expected model augmentation based on the golden section (GSAG-EMA) algorithm. The approach adaptively adjusts both the model structure and parameters in a grid-based format to accommodate the varying maneuvering patterns. It incorporates both local and global weighting schemes, with two models within the grid based on the golden section. Furthermore, the transition probability matrix is dynamically updated following specific rules, and the execution strategy for each module is determined according to the filtering results. Simulation results under both weak and strong maneuvering scenarios demonstrate that the proposed GSAG-EMA algorithm consistently outperforms the IMM-based, EMA, and AG-BMA algorithms in terms of root mean square error (RMSE) and Hausdorff distance, thereby substantiating its superior tracking performance. Full article
(This article belongs to the Special Issue Radar Data Processing and Analysis)
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20 pages, 1857 KB  
Article
Multi-Information-Assisted Joint Detection and Tracking of Ground Moving Target for Airborne Radar
by Ran Liu, Xiangqian Li, Jinping Sun and Tao Shan
Remote Sens. 2025, 17(12), 2093; https://doi.org/10.3390/rs17122093 - 18 Jun 2025
Viewed by 538
Abstract
Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a [...] Read more.
Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, enabling bidirectional data exchange between the detector and tracker for multiple ground targets. An adaptive threshold detector is developed by incorporating a priori information and tracker feedback. Additionally, we innovatively propose an improved Variable Structure Interacting Multiple Model (VS-IMM) filter that leverages road network constraints and detector outputs for tracking, featuring an enhanced model probability calculation to significantly reduce computational time. Simulation results demonstrate that the proposed method significantly improves data association accuracy and tracking precision. Full article
(This article belongs to the Special Issue Radar Data Processing and Analysis)
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25 pages, 3124 KB  
Article
Extended Object Tracking Using an Orientation Vector Based on Constrained Filtering
by Zheng Wen, Le Zheng and Tao Zeng
Remote Sens. 2025, 17(8), 1419; https://doi.org/10.3390/rs17081419 - 16 Apr 2025
Cited by 2 | Viewed by 582
Abstract
In many extended object tracking applications (e.g., tracking vehicles using a millimeter-wave radar), the shape of an extended object (EO) remains unchanged while the orientation angle varies over time. Thus, tracking the shape and the orientation angle as individual parameters is reasonable. Moreover, [...] Read more.
In many extended object tracking applications (e.g., tracking vehicles using a millimeter-wave radar), the shape of an extended object (EO) remains unchanged while the orientation angle varies over time. Thus, tracking the shape and the orientation angle as individual parameters is reasonable. Moreover, the tight coupling between the orientation angle and the heading angle contains information on improving estimation performance. Hence, this paper proposes a constrained filtering approach utilizing this information. First, an EO model is built using an orientation vector with a heading constraint. This constraint is formulated using the relation between the orientation vector and the velocity vector. Second, based on the proposed model, a variational Bayesian (VB) approach is proposed to estimate the kinematic, shape, and orientation vector states. A pseudo-measurement is constructed from the heading constraint and is incorporated into the VB framework. The proposed approach can also address the ambiguous issue in orientation angle estimation. Simulation and real-data results are presented to illustrate the effectiveness of the proposed model and estimation approach. Full article
(This article belongs to the Special Issue Radar Data Processing and Analysis)
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20 pages, 857 KB  
Article
Gridless Parameter Estimation for Pulse–Doppler Radar Under Limited Bit Budgets
by Yating Wang, Guanqi Tong, Feng Xi, Shengyao Chen and Zhong Liu
Remote Sens. 2025, 17(6), 982; https://doi.org/10.3390/rs17060982 - 11 Mar 2025
Viewed by 787
Abstract
In this work, we investigate the gridless parameter estimation of pulse–Doppler radar targets using a reduced number of samples under a limited bit budget. We propose a hybrid analog and digital (HAD) acquisition system integrating a tunable analog component, low-resolution quantizers, and a [...] Read more.
In this work, we investigate the gridless parameter estimation of pulse–Doppler radar targets using a reduced number of samples under a limited bit budget. We propose a hybrid analog and digital (HAD) acquisition system integrating a tunable analog component, low-resolution quantizers, and a digital filter. Under the framework of task-based quantization, the HAD architecture is designed to optimize target parameter estimation within the constraints of the bit budget. Specifically, a small subset of the received signal samples is observed and the low-rank parameter matrix is recovered using matrix completion techniques. The atomic norm minimization method is applied to reconstruct the complete parameter matrix, enabling gridless estimation of the parameters. Numerical experiments are conducted to validate the effectiveness of the proposed receiver in gridless parameter estimation. Full article
(This article belongs to the Special Issue Radar Data Processing and Analysis)
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