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Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 4070

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Prof. Dr. Wei Wang

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1. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
2. Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: spaceborne SAR system design and signal processing: radar waveform design; multichannel and digital beamforming
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Prof. Dr. Wei Yang

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School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
Interests: moving target detection; machine learning method on SAR; SAR 3-D imaging
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Prof. Dr. Guangcai Sun

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National Key Laboratory of Radar Signal Processing，Xidian University，Xi’an，710071，China
Interests: imaging of several SAR modes; moving target detection and imaging
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Prof. Dr. Jiaqi Chen

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College of Information Science and Engineering, Hohai University, Changzhou 213200, China
Interests: synthetic aperture radar; image processing and signal processing
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Prof. Dr. Ning Li

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School of Computer and Information Engineering, Henan University, Kaifeng 475004, China
Interests: synthetic aperture radar; radar imaging; radar image processing; radar jamming
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Dr. Yongwei Zhang

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Guest Editor

Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: HRWS SAR system; SAR waveform design; MIMO-SAR system

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) is an advanced remote sensing technology than can generate wide-swath and high-resolution images. Unlike optical sensors, it can facilitate all-day and all-weather Earth observation, and therefore is widely employed in environmental monitoring, disaster management, agriculture, forestry, and oceanography applications.

In recent years, a wide range of new SAR system architectures and technologies have emerged: SAR systems work from single-mode operation to having multifunctional and multi-mode capabilities, and transition from single-channel transmit/receive systems to single-transmit and multi-reception systems, enabling high resolution and wide-swath imaging simultaneously. Further, they transform into multiple-input multiple-output (MIMO) SAR systems, significantly increasing the system’s degrees of freedom, and from single-baseline, single-frequency, single-polarization configurations into multi-baseline, multi-frequency, and multi-polarization systems with the capabilities of jamming and anti-jamming. They also transition from sensing imaging systems into adaptive SAR systems with flexible transmit/receive capabilities, and further into cognitive SAR systems capable of logical reasoning in complex environments. Additionally, they are transformed from large, high-cost monolithic SAR platforms to lightweight, low-cost distributed SAR constellations. Finally, their resolution improves from having a precision of hundreds of meters to having sub-meter and even centimeter-level precision. These new-generation technologies continuously expand the SAR system's degrees of freedom to meet increasing application demands for multi-dimensional spatial sensing, high-resolution and wide-swath coverage, etc.

This Special Issue focuses on reporting the latest progress in the Synthetic Aperture Radar (SAR) system signal processing and applications. Specifically, it includes (but is not limited to) the research of advanced technology and the newest concepts for microwave imaging and applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

High-resolution and wide-swath (HRWS) SAR systems and signal processing;
MIMO SAR technology;
Distributed SAR technology;
Adaptive/cognitive SAR systems;
Digital beamforming for SAR systems;
Waveform diversity for SAR systems;
SAR jamming and anti-jamming;
Ambiguity suppression technology;
Multidimensional SAR imaging;
AI for SAR imaging and applications;
New concepts of future SAR systems.

Prof. Dr. Wei Wang
Prof. Dr. Wei Yang
Prof. Dr. Guangcai Sun
Prof. Dr. Jiaqi Chen
Prof. Dr. Ning Li
Dr. Yongwei 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-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

advanced SAR systems
ambiguity suppression
anti-jamming
multidimensional imaging
new concepts for SAR systems

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Published Papers (8 papers)

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Research

28 pages, 15563 KB

Open AccessArticle

Rapid Detection of Ionospheric Disturbances in L-Band InSAR Systems: A Case Study Using LT-1 Data

by Huaishuai Wang, Hongjun Song, Yulun Wu, Yang Liu, Jili Wang and Xiang Zhang

Remote Sens. 2026, 18(7), 1030; https://doi.org/10.3390/rs18071030 - 29 Mar 2026

Viewed by 325

Abstract

Ionospheric effects constitute a key error source limiting the accuracy of surface deformation monitoring using L-band interferometric synthetic aperture radar (InSAR). Efficient identification of interferometric pairs affected by ionospheric disturbances is therefore essential for large-scale and high-throughput automated InSAR processing. To address this issue, a parameterized ionospheric detection method based on azimuth offsets derived from sub-aperture images is proposed. The proposed method integrates random-sampling pixel offset tracking (RS-POT) with piecewise Gaussian fitting to enable rapid and robust detection of ionospheric disturbances. Experimental validation was conducted using 50 interferometric pairs acquired by the LuTan-1 (LT-1) satellite, China’s first dual-satellite L-band SAR mission, covering high-, mid-, and low-latitude regions with varying ionospheric conditions. The results demonstrate that the proposed method can reliably identify ionospheric disturbances under diverse conditions while maintaining high computational efficiency. The proposed framework provides an effective solution for determining whether ionospheric correction is required, thereby improving the efficiency of automated interferometric processing workflows. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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32 pages, 103163 KB

Open AccessArticle

Spatiotemporal Prediction and Pattern Analysis of Complex Ground Deformation Fields from Multi-Temporal InSAR

by Yuanzhao Fu, Jili Wang, Yi Zhang, Heng Zhang, Yulun Wu and Litao Kang

Remote Sens. 2026, 18(6), 925; https://doi.org/10.3390/rs18060925 - 18 Mar 2026

Viewed by 287

Abstract

Ground deformation is a major geohazard in many urban areas, requiring reliable monitoring and forecasting for hazard mitigation. Although Multi-Temporal InSAR enables high-resolution deformation monitoring, most prediction approaches rely on single-point modeling and fail to exploit spatial dependencies within deformation fields. This study proposes a spatiotemporally synchronous prediction framework for large-scale InSAR deformation fields, integrating sequence preprocessing, spatiotemporal modeling, and deformation pattern analysis. First-order differencing reduces sequence non-stationarity, while a patch-based encoder-decoder structure preserves spatial topology during dimensionality reduction. The core prediction model, built on PredRNNv2, captures the long-term spatiotemporal evolution of InSAR deformation sequences. In addition, independent component analysis (ICA) combined with K-means clustering identifies dominant deformation patterns and their geological associations. The framework is evaluated using synthetic datasets simulating multiple deformation mechanisms and Sentinel-1 InSAR time-series data over the Beijing Plain from 2015 to 2025. Results show that the model accurately captures deformation evolution and identifies transitions associated with groundwater regulation. These findings demonstrate the potential of deep spatiotemporal learning for large-scale InSAR deformation prediction and geohazard mechanism interpretation. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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28 pages, 9208 KB

Open AccessArticle

Knowledge-Aided Multichannel SAR Clutter Suppression Algorithm in Complex Scenes

by Yun Zhang, Niezipeng Kang, Zuzhen Huang, Qinglong Hua and Hang Ren

Remote Sens. 2026, 18(6), 879; https://doi.org/10.3390/rs18060879 - 12 Mar 2026

Viewed by 233

Abstract

Multichannel synthetic aperture radar (SAR) achieves high-resolution imaging while significantly enhancing the spatial freedom of the SAR system. As SAR hardware performance continues to improve, observed scenes often transition from simple to complex scenes. The increasingly complex clutter components introduced by complex scenes make clutter suppression increasingly challenging. Traditional multichannel clutter suppression algorithms usually assume that the observed scene, as a whole, satisfies the independent and identical distribution (IID) condition. However, in actual complex scenes, this assumption often proves difficult to uphold. Therefore, how to achieve more effective clutter suppression for complex scenes is a challenge for SAR. In this paper, we propose a knowledge-aided (KA) multichannel SAR clutter suppression algorithm for complex scenes. First, the single-channel image is processed at the superpixel level and a superpixel fusion algorithm is proposed, which adaptively realizes the refined classification of the complex scene. Then, a two-step clutter suppression processing method with multi-strategy clutter suppression preprocessing and sparse Bayesian residual clutter suppression is proposed. This method not only provides effective classification information for complex scenes but also achieves more efficient clutter suppression in complex scenes based on this classification information. Finally, the clutter suppression performance of this algorithm in complex scenes was validated through measured data. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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19 pages, 18959 KB

Open AccessArticle

Determination of Slow Surface Movements Around the 1915 Çanakkale Bridge During the 2022–2024 Period with Sentinel-1 Time Series

by Duygu Arikan Ispir and Hasan Bilgehan Makineci

Remote Sens. 2026, 18(6), 858; https://doi.org/10.3390/rs18060858 - 11 Mar 2026

Viewed by 341

Abstract

This study applied SBAS-InSAR to a dense Sentinel-1 Single Look Complex (SLC) archive (146 scenes) to monitor the 1915 Çanakkale Bridge between 2022 and 2024 (data up to 7 January 2025 were available and considered in the time-series reconstruction). The analysis produced LOS mean velocity maps and pointwise displacement time series, revealing localized displacement concentrated near the Lapseki approach. Extreme LOS values reached approximately −101 mm (min) and +77 mm (max) across the domain, while maximum cumulative LOS displacement near the Asian anchorage approached −90 mm. These satellite observations suggest that ground-related processes may contribute to the detected observed movement; however, LOS-only measurements and limited in situ validations preclude a definitive separation between structural and geotechnical drivers. We therefore recommend targeted GNSS/levelling campaigns, ascending (ASC)–descending (DSC) InSAR fusion, and formal uncertainty reporting to better constrain the deformation sources and magnitude. The study concluded that the SBAS-InSAR method is effective for long-term, contactless monitoring of bridges and similar mega structures. It was also determined that this method can be used to identify critical areas requiring ongoing monitoring. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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21 pages, 5182 KB

Open AccessArticle

Quantitative Assessment of the Computing Performance for the Parallel Implementation of a Time-Domain Airborne SAR Raw Data Focusing Procedure

by Jorge Euillades, Paolo Berardino, Carmen Esposito, Antonio Natale, Riccardo Lanari and Stefano Perna

Remote Sens. 2026, 18(2), 221; https://doi.org/10.3390/rs18020221 - 9 Jan 2026

Viewed by 451

Abstract

In this work, different implementation strategies for a Time-Domain (TD) focusing procedure applied to airborne Synthetic Aperture Radar (SAR) raw data are presented, with the key objective of quantitatively assessing their computing time. In particular, two methodological approaches are proposed: a pixel-wise strategy, which processes each image pixel independently, and a matrix-wise strategy, which handles data blocks collectively. Both strategies are further extended to parallel execution frameworks to exploit multi-threading and multi-node capabilities. The presented analysis is conducted within the context of the airborne SAR infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) in Naples, Italy. This infrastructure integrates an airborne SAR sensor and a high-performance Information Technology (IT) platform well-tailored to the parallel processing of huge amounts of data. Experimental results indicate an advantage of the pixel-wise strategy over the matrix-wise counterpart in terms of computing time. Furthermore, the adoption of parallel processing techniques yields substantial speedups, highlighting its relevance for time-critical SAR applications. These findings are particularly relevant in operational scenarios that demand a rapid data turnaround, such as near-real-time airborne monitoring in emergency response contexts. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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25 pages, 72453 KB

Open AccessArticle

Fast Low-Artifact Image Generation for Staggered SAR: A Preview-Oriented Method

by Sixi Hou, Jinsong Qiu, Yunkai Deng, Heng Zhang, Wei Wang, Huaitao Fan, Zhen Chen, Qingchao Zhao and Fengjun Zhao

Remote Sens. 2026, 18(1), 83; https://doi.org/10.3390/rs18010083 - 25 Dec 2025

Viewed by 543

Abstract

Staggered synthetic aperture radar (SAR) is an innovative concept capable of achieving an ultrawide continuous swath with fine azimuth resolution by variable pulse repetition interval. However, the inherent data gaps and nonuniform sampling introduce severe azimuth artifacts, degrading image quality. Existing methods can mitigate these artifacts but struggle to effectively balance imaging quality and computational cost, especially under low oversampling conditions. To address this challenge, this paper proposes a low-artifact preview image generation method for staggered SAR. First, the artifact characteristics are analyzed through the derivation of a staggered SAR signal model. Then, a three-stage processing framework is introduced, consisting of constant-gradient phase extrapolation, artifact-based inverse filtering, and result fusion. Additionally, data nonuniformity is addressed using a weighted nonuniform fast Fourier transform. Simulation results demonstrate that the proposed method significantly improves processing speed compared to existing techniques while maintaining good imaging quality, making it suitable for rapid scene screening in wide-area SAR applications. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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26 pages, 7980 KB

Open AccessArticle

A Novel Data-Focusing Method for Highly Squinted MEO SAR Based on Spatially Variable Spectrum and NUFFT 2D Resampling

by Huguang Yao, Tao He, Pengbo Wang, Zhirong Men and Jie Chen

Remote Sens. 2026, 18(1), 49; https://doi.org/10.3390/rs18010049 - 24 Dec 2025

Viewed by 448

Abstract

Although the elevated orbit and highly squinted observation geometry bring advantages for medium-earth-orbit (MEO) synthetic aperture radar (SAR) in applications, they also complicate signal processing. The severe spatial variability of Doppler parameters and large extended range distribution of echo make it challenging for the traditional imaging algorithms to get the expected results. To quantify the variation, a spatially variable two-dimensional (SV2D) spectrum is established in this paper. The sufficient order and spatially variable terms allow it to preserve the features of targets both in the scene center and at the edge. In addition, the huge data volume and incomplete azimuth signals of edge targets, caused by the large range walk when MEO SAR operates in squinted mode, are alleviated by the variable pulse repetition interval (VPRI) technique. Based on this, a novel data-focusing method for highly squinted MEO SAR is proposed. The azimuth resampling, achieved through the non-uniform fast Fourier transform (NUFFT), eliminates the impact of most Doppler parameter space variation. Then, a novel imaging kernel is applied to accomplish target focusing. The spatially variable range cell migration (RCM) is corrected by a similar idea, with Doppler parameter equalization, and an accurate high-order phase filter derived from the SV2D spectrum guarantees that the targets located in the center range gate and the center Doppler time are well focused. For other targets, inspired by the non-linear chirp scaling algorithm (NCSA), the residual spatially variable mismatch is eliminated by a cubic phase filter during the scaling process to achieve sufficient focusing depth. The simulation results are given at the end of this paper and these validate the effectiveness of the method. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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30 pages, 3829 KB

Open AccessArticle

MFE-STN: A Versatile Front-End Module for SAR Deception Jamming False Target Recognition

by Liangru Li, Lijie Huang, Tingyu Meng, Cheng Xing, Tianyuan Yang, Wangzhe Li and Pingping Lu

Remote Sens. 2025, 17(23), 3848; https://doi.org/10.3390/rs17233848 - 27 Nov 2025

Cited by 1 | Viewed by 618

Abstract

Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability essential for conventional recognition algorithms. To address this problem, we propose a versatile front-end Multi-Feature Extraction and Spatial Transformation Network (MFE-STN), specifically designed for the task of discriminating between true targets and deceptive false targets created by SAR jamming, which can be seamlessly integrated with existing CNN backbones without architecture modification. MFE-STN integrates three complementary operations: (i) wavelet decomposition to extract the overall geometric features and scattering distribution of the target, (ii) a manifold transformation module for non-linear alignment of heterogeneous feature spaces, and (iii) a lightweight deformable spatial transformer that compensates for local geometric distortions introduced by deceptive jamming. By analyzing seven typical parameter-mismatch effects, we construct a simulated dataset containing six representative classes—four known classes and two unseen classes. Experimental results demonstrate that inserting MFE-STN boosts the average F1-score of known targets by 12.19% and significantly improves identification accuracy for unseen targets. This confirms the module’s capability to capture discriminative signatures to distinguish genuine targets from deceptive ones while exhibiting strong cross-domain generalization capabilities. Full article

(This article belongs to the Special Issue Advances in Synthetic Aperture Radar (SAR) System, Signal Processing and Applications)

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