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Advances in Synthetic Aperture Radar (SAR) Imaging and Time-Varying Scattering Target Interaction: Innovation, Theory, and Applications

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 3909

Editors

Prof. Dr. Dejun Feng

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: SAR imaging; SAR countermeasure; SAR target feature transformation
Dr. Junjie Wang

E-Mail Website
Guest Editor
State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics & Information System, National University of Defense Technology, Changsha 410073, China
Interests: SAR signal processing; SAR jamming
Special Issues, Collections and Topics in MDPI journals
Dr. Guang Sun

E-Mail Website
Guest Editor
State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System, National University of Defense Technology, Changsha, China
Interests: radar cross-sections; stability analysis; pin photodiodes; time-varying; metasurfaces
Dr. Guodong Jin

E-Mail Website
Guest Editor
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: apaceborne HRWS SAR system design and ambiguity suppression
Special Issues, Collections and Topics in MDPI journals
Dr. Shaoqiu Song

E-Mail
Guest Editor Assistant
College of Electronic Science and Technology, National University of Defense Technology, Changsha, China
Interests: synthetic aperture radar imaging; SAR Image processing

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR), as the core remote sensing technology for Earth observation, has superior imaging capabilities based on the fundamental assumption of "static observation scene". However, a new category of targets with time-varying scattering characteristics—such as time-varying metasurface and mechanically adjustable targets—is challenging this theoretical foundation. These targets exert a dual impact on SAR remote sensing. On one hand, this phenomenon severely disrupts traditional SAR imaging mechanisms, leading to image distortions including defocusing, ghosting, and even target annihilation. These issues directly degrade the geometric and radiometric accuracy of SAR image targets, posing significant challenges to subsequent target feature extraction and interpretation. On the other hand, it has also given rise to unprecedented remote sensing applications. The active manipulation of the scattering characteristics of targets provides the possibility for realizing new remote sensing concepts, such as dynamic camouflage, target calibration, and physical layer information embedding.

This Special Issue aims to deeply explore the impact and insights of time-varying scattering targets characteristics on SAR imaging from the perspective of remote sensing and information extraction. We not only focus on the negative effects of such targets on image quality and corresponding mitigation strategies, but also commit to exploring how to leverage this phenomenon to develop new capabilities in remote sensing perception.

This Special Issue aims to systematically explore the fundamental transformations brought by targets with time-varying scattering characteristics to the field of SAR remote sensing. We are committed to the following objectives:

Reveal the mechanism by which time-varying scattering affects the information capacity, calibration accuracy, and target interpretation capability of SAR images.

Develop innovative SAR imaging theories and signal processing technologies that can perceive, model, and reconstruct dynamically scattering scenes.

Explore the remote sensing applications of time-varying targets in directions such as new-type calibration, performance verification, and intelligent environment construction of SAR systems.

This theme is highly aligned with the positioning of the Remote Sensing journal, as it directly addresses the paradigm shift in remote sensing frontier from "static observation" to "dynamic interaction". Relevant research will deepen our understanding of the interaction mechanism between radar and the environment, which not only relates to the reliability and credibility of remote sensing data but also promotes the development of active and cognitive remote sensing technologies. This holds profound significance for ensuring the accuracy of remote sensing information and expanding its application boundaries.

In this Special Issue, original research papers and review articles on the following topics are welcome. The research fields may include (but are not limited to) the following:

(1) SAR/PolSAR/ISAR Image Processing;

(2) Time-Varying Target Feature Analysis for SAR Images;

(3) SAR Target Detection and Recognition;

(4) Advanced SAR/PolSAR/ISAR passive countermeasure technology;

(5) SAR Image Adversarial Sample Generation;

(6) Intelligent SAR Image Countermeasure and Evaluation;

(7) SAR/PolSAR/ISAR Image Feature Transformation;

(8) Remote Sensing Assisted by Metasurfaces.

Prof. Dr. Dejun Feng
Dr. Junjie Wang
Dr. Guang Sun
Dr. Guodong Jin
Guest Editors

Dr. Shaoqiu Song
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

  • Synthetic Aperture Radar (SAR)
  • Polarized Synthetic Aperture Radar (PolSAR)
  • SAR imaging
  • SAR target feature extraction
  • SAR countermeasure
  • SAR target feature transformation
  • SAR target recognition

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Published Papers (5 papers)

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Research

27 pages, 7755 KB  
Article
A Fast ISAR Imaging Method Based on PC-2D-FIR-GEM-Net for Low SNR and Sparse Aperture Conditions
by Kewei Zhou, Guanghu Jin, Feng He, Zhihua He and Linjie Cai
Remote Sens. 2026, 18(15), 2505; https://doi.org/10.3390/rs18152505 - 1 Aug 2026
Viewed by 276
Abstract
High-resolution inverse synthetic aperture radar (ISAR) imaging under low signal-to-noise ratio (SNR) and sparse-aperture conditions remains challenging due to severe sidelobe artifacts, weak-scatterer loss, and high computational burden. Although sparse Bayesian learning (SBL) methods are robust to noise, most existing formulations assign pixel-wise [...] Read more.
High-resolution inverse synthetic aperture radar (ISAR) imaging under low signal-to-noise ratio (SNR) and sparse-aperture conditions remains challenging due to severe sidelobe artifacts, weak-scatterer loss, and high computational burden. Although sparse Bayesian learning (SBL) methods are robust to noise, most existing formulations assign pixel-wise independent hyperparameters to image coefficients, which limits their ability to characterize the spatial clustering of scattering centers. Moreover, conventional Bayesian inference often involves large-scale matrix inversion and iterative optimization, leading to high computational cost. To address these issues, this paper proposes a fast ISAR imaging method termed pattern-coupled (PC) two-dimensional (2D) fast inverse-free reconstruction (FIR) generalized expectation-maximization (GEM) network (PC-2D-FIR-GEM-Net), which integrates pattern-coupled hierarchical Bayesian modeling, inverse-free generalized expectation-maximization (GEM) inference, and model-driven deep unfolding. A pattern-coupled prior is first introduced to exploit local structural dependencies among neighboring scatterers, which improves the recovery of weak and clustered scattering structures. Then, an inverse-free GEM solver is developed by constructing surrogate objectives so that image updating can be performed without explicit matrix inversion. Finally, the iterative solver is unfolded into a finite-stage network, where a lightweight convolutional neural network (CNN) learns the coupled precision field and stage-wise update parameters while preserving the model-driven inverse-free update structure. Experimental results on both simulated and measured ISAR datasets demonstrate that the proposed method achieves improved focusing quality, better structural preservation, and significantly reduced computational time under challenging sparse-aperture and low-SNR conditions. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Time-Varying Scattering Target Interaction: Innovation, Theory, and Applications)
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23 pages, 4940 KB  
Article
Coherent Integration for Cooperative Bistatic Radar with Joint Time-Domain Waveform Agility
by Yiyue Liu, Jiapeng Yin, Yukai Kong and Weidong Hu
Remote Sens. 2026, 18(13), 2081; https://doi.org/10.3390/rs18132081 - 25 Jun 2026
Viewed by 406
Abstract
Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and [...] Read more.
Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and phase evolution induced by synchronization. This paper develops a joint coherent integration method for a cooperative bistatic radar with simultaneous pulse width (PW) and pulse repetition interval (PRI) agility. Firstly, we establish and analyze a bistatic geometric model to reveal key integration problems under agile waveforms, and then derive the coherent processing interval (CPI) local polynomial description for bistatic delay, Doppler and acceleration. On this basis, the matched filter response of each agile pulse is analyzed under the fixed-bandwidth assumption with linear frequency modulation (LFM), showing that PW agility produces a compressed peak displacement and an additional deterministic phase term, whereas PRI agility converts slow-time coherent integration into a nonuniformly sampled spectral estimation problem. To solve this problem, a joint fast and slow-time compensation route is derived, together with a bistatic-specific parameter design method that connects coherent integration tolerances with the bistatic angle and the observable projection vector. Finally, we test the performance of the proposed joint integration method in multiple scenarios and verify its effectiveness and robustness, which enhances detection performance and resolution for target localization. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Time-Varying Scattering Target Interaction: Innovation, Theory, and Applications)
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25 pages, 8495 KB  
Article
Variable Frequency Phase Modulation on Time-Modulated Metasurface for SAR Feature Reconstruction
by Yumeng Fang, Junjie Wang, Guang Sun and Dejun Feng
Remote Sens. 2026, 18(7), 1060; https://doi.org/10.3390/rs18071060 - 1 Apr 2026
Cited by 1 | Viewed by 906
Abstract
Time-modulated metasurfaces offer a novel technical approach for actively modulating and reconstructing radar target characteristics through their dynamic control of electromagnetic waves. However, existing SAR feature reconstruction methods based on metasurfaces are typically constrained by a one-to-one mapping mechanism where “a single metasurface [...] Read more.
Time-modulated metasurfaces offer a novel technical approach for actively modulating and reconstructing radar target characteristics through their dynamic control of electromagnetic waves. However, existing SAR feature reconstruction methods based on metasurfaces are typically constrained by a one-to-one mapping mechanism where “a single metasurface unit corresponds to a single scattering center”. This results in low reconstruction efficiency and limited flexibility, hindering high-fidelity simulation of complex multi-scatterer targets. Therefore, this paper proposes a variable frequency-phase modulation method on time-modulated metasurfaces for SAR feature reconstruction. The core concept of this method involves decomposing complex targets into discrete scattering centers. By employing a “frequency-modulated continuous-phase modulation” strategy, a tailored modulation scheme is designed for each time-modulated metasurface, generating multiple adjustable false scattering center arrays in both the range and elevation dimensions of SAR imagery. Experimental results demonstrate that this method can effectively reconstruct SAR signatures highly similar to the original target, with similarity metrics exceeding 0.9. This study marks the first systematic application of frequency-modulation techniques to SAR signature reconstruction, breaking through the inherent limitations of traditional one-to-one mapping. It provides a novel theoretical framework and technical solution for achieving efficient, flexible, and high-fidelity simulation of complex target electromagnetic signatures, holding significant application value in fields such as radar countermeasures and signature camouflage. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Time-Varying Scattering Target Interaction: Innovation, Theory, and Applications)
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25 pages, 4978 KB  
Article
Full Polarimetric Scattering Matrix Estimation with Single-Channel Echoes via Time-Varying Polarization Modulation
by Yan Chen, Zhanling Wang, Zhuang Wang and Yongzhen Li
Remote Sens. 2026, 18(6), 870; https://doi.org/10.3390/rs18060870 - 11 Mar 2026
Cited by 2 | Viewed by 675
Abstract
Polarimetric information is essential for scattering interpretation and target characterization in synthetic aperture radar (SAR) remote sensing, yet many resource-constrained platforms (e.g., small satellites and unmanned aerial vehicles (UAVs)) operate with limited polarization modes or even a single radio frequency (RF) chain, which [...] Read more.
Polarimetric information is essential for scattering interpretation and target characterization in synthetic aperture radar (SAR) remote sensing, yet many resource-constrained platforms (e.g., small satellites and unmanned aerial vehicles (UAVs)) operate with limited polarization modes or even a single radio frequency (RF) chain, which limits full polarimetric scattering acquisition. To address this limitation, this paper proposes a single-channel framework for estimating the full polarization scattering matrix (PSM) enabled by time-varying polarization modulation. The transmit/receive polarization states are steered along predefined trajectories on the Poincaré sphere to generate time-varying polarization tags that are encoded into the received echoes through the target’s polarization-varying response. A compact observation model is then derived to relate the single-channel echoes, the known polarization tags, and the unknown PSM; based on this, the PSM is then estimated via a least squares formulation with a low-rank approximation. Simulation results demonstrate the robust reconstruction of the full polarimetric scattering matrix under diverse modulation trajectories. For arbitrarily chosen random point targets, when the signal-to-noise ratio (SNR) exceeds −20 dB, the polarimetric similarity coefficient approaches 1, and the estimation errors of Pauli power components converge toward zero. Furthermore, the method’s reliability is validated on distributed vegetation clutter. Quantitative metrics demonstrate near-perfect statistical consistency, with polarimetric entropy and alpha angle errors within 0.14%. Overall, the proposed approach provides a practical pathway to enhance the availability of full polarimetric scattering information under limited-observation conditions, confirming its feasibility for downstream analysis in complex natural scenes while maintaining a single radio frequency (RF) chain architecture augmented by a polarization modulator. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Time-Varying Scattering Target Interaction: Innovation, Theory, and Applications)
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23 pages, 9839 KB  
Article
Robust Multi-Target ISAR Imaging at Low SNR Based on Particle Swarm Optimization and Sequential Variational Mode Decomposition
by Xinyuan Tong, Yulin Le, Yinghong Liu, Xiaotao Huang and Chongyi Fan
Remote Sens. 2026, 18(5), 830; https://doi.org/10.3390/rs18050830 - 7 Mar 2026
Cited by 1 | Viewed by 838
Abstract
The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at [...] Read more.
The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at low SNR. To address these issues, this paper proposes a novel robust imaging framework. The framework is built upon two key innovations: a partitioned block-wise compensation mechanism integrated with PSO for simultaneous and precise motion parameters estimation of multiple targets, which avoids local optima and error accumulation; and the application of Sequential Variational Mode Decomposition (SVMD) to adaptively separate and reconstruct signals, thereby suppressing inter-target aliasing and noise interference overlooked in prior studies. Simulations and measured-data experiments confirm that the proposed method maintains clear focusing and superior image quality even at low SNR, outperforming existing techniques in terms of image entropy, contrast, and resolution. This paper provides a robust and effective solution for high-resolution radar surveillance in complex multi-target scenarios. Full article
(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) Imaging and Time-Varying Scattering Target Interaction: Innovation, Theory, and Applications)
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