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Applications of Synthetic Aperture Radar to Target Detection and Tracking
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Applications of Synthetic Aperture Radar to Target Detection and Tracking

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 23713

Special Issue Editors

Dr. Zhongyu Li

E-Mail Website
Guest Editor
The School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: SAR target detection and imaging
Special Issues, Collections and Topics in MDPI journals
Dr. Lin Gao

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: multitarget tracking; information fusion; SLAM; statistical signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Giorgio Battistelli

E-Mail Website
Guest Editor
Dipartimento di Ingegneria dell\'Informazione (Department of Information Engineering), University of Florence, Via Santa Marta 3, 50139 Firenze, Italy
Interests: sensor networks; large-scale systems; information fusion; data fusion; distributed estimation; tracking; random finite sets; networked control systems
Prof. Dr. Vito Pascazio

E-Mail Website
Guest Editor
Department of Engineering, Università di Napoli “Parthenope”, Centro Direzionale Isola C4, 80143 Napoli, Italy
Interests: synthetic aperture radar (SAR) image processing; SAR interferometry and tomography; ground-based SAR; microwave tomographic image reconstruction; ground-penetrating radars; biomedical image processing; magnetic resonance imaging; image processing; image compression; compressive sensing; linear and nonlinear statistical signal processing; Markov random field
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synthetic aperture radar (SAR) is an important active microwave imaging system, and it has been extensively applied in the remote sensing community, e.g., geological exploration, disaster forecast, traffic monitoring, iceberg observation, etc. due to its high resolution, all-day and all-weather imaging capability of the observation area. In recent years, with the development of many kinds of electronic technologies such as Terahertz technology and Microwave photonic technology, the SAR system can achieve ultra-high-resolution imaging, capable of delicate images of some valuable SAR targets of interest. Meanwhile, to meet the increasing requirements of surveillance systems, target detection, imaging, and tracking has been a long-standing interest subject for the SAR community, as valuable target information is important for wide area surveillance systems with limited revisit times. Therefore, SAR target detection, imaging, and tracking is very import for remote sensing. However, complicated target characteristics, complex environment/interference and complicated non-cooperative target movements have made great challenges to SAR target detection, imaging, and tracking. Along with appearances of new challenges and processing techniques, there are still many research issues on SAR target detection, tracking, and imaging, such as the characteristics modeling, clutter and interference suppression, target component information extraction, target micro-Doppler extraction and application, and combination of artificial intelligence techniques, among others.

This Special Issue aims to collect and highlight outstanding contributions that cover “Applications of Synthetic Aperture Radar to Target Detection and Tracking”, including (but not limited to):

  • SAR target modeling and characteristic analysis;
  • SAR target detection, tracking and imaging in ground/sea environment;
  • SAR target detection, tracking and imaging in interference situation;
  • SAR clutter and interference suppression technique;
  • Detection and tracking using SAR images with applications in geology;
  • SAR target component information extraction;
  • SAR target micro-Doppler extraction and application;
  • Combination of artificial intelligence techniques.

We are looking forward to receiving your contribution to this Special Issue entitled “Applications of Synthetic Aperture Radar to Target Detection and Tracking”.

Dr. Zhongyu Li
Dr. Lin Gao
Prof. Dr. Giorgio Battistelli 
Prof. Dr. Vito Pascazio 
Guest Editors

Manuscript Submission Information

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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

  • SAR image processing
  • extended objects detection and tracking
  • micro-doppler signatures
  • information fusion
  • clutter and interference suppression
  • information extraction
  • artificial intelligence

Published Papers (15 papers)

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Research

19 pages, 8760 KiB  
Article
A Novel Bistatic SAR Maritime Ship Target Imaging Algorithm Based on Cubic Phase Time-Scaled Transformation
by Qing Yang, Zhongyu Li, Junao Li, Hongyang An, Junjie Wu, Yiming Pi and Jianyu Yang
Remote Sens. 2023, 15(5), 1330; https://doi.org/10.3390/rs15051330 - 27 Feb 2023
Cited by 2 | Viewed by 1101
Abstract
Due to the advantages of flexible configuration, bistatic synthetic aperture radar (BiSAR) has the ability to effectively observe from various visual angles, such as forward view area and squint area, and has good anti-jamming characteristics. It can be applied to the surveillance of [...] Read more.
Due to the advantages of flexible configuration, bistatic synthetic aperture radar (BiSAR) has the ability to effectively observe from various visual angles, such as forward view area and squint area, and has good anti-jamming characteristics. It can be applied to the surveillance of ship targets on the sea and is gradually gaining an increasing amount of attention. However, for ship targets with complex motions on the sea surface, such as maneuvering targets or ship targets under high sea conditions, the high-order Doppler frequency of the scattering points is always spatial variation (related to the spatial position of scattering points), which poses a considerable challenge for the imaging of maritime ship targets in BiSAR. To resolve this problem, a BiSAR maritime ship target imaging algorithm based on cubic phase time-scaled transformation is proposed in this paper. First, through pre-processing of echo such as Doppler prefiltering and keystone transform, the translation compensation of the BiSAR maritime ship target is completed, and the scattering point energy is corrected to within one range unit. Then, the azimuth signal is modeled as a multi-component cubic phase signal. Based on the proposed cubic phase time-scaled transformation, the Doppler centroid, frequency rate, and third-order frequency of scattering points are estimated. Eventually, the BiSAR imaging of maritime ship targets is realized. This algorithm has excellent noise immunity and low cross-terms. The simulation leads to the verification of the validity of the proposed algorithm. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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19 pages, 1644 KiB  
Article
Fast Resolution Enhancement for Real Beam Mapping Using the Parallel Iterative Deconvolution Method
by Ping Zhang, Yongchao Zhang, Deqing Mao, Jianan Yan and Shuaidi Liu
Remote Sens. 2023, 15(4), 1164; https://doi.org/10.3390/rs15041164 - 20 Feb 2023
Viewed by 1281
Abstract
Super-resolution methods for real beam mapping (RBM) imagery play a significant role in many microwave remote sensing applications. However, the existing super-resolution methods require high-dimensional matrix operations in the case of wide-field imaging, which makes it difficult to satisfy the requirements of real-time [...] Read more.
Super-resolution methods for real beam mapping (RBM) imagery play a significant role in many microwave remote sensing applications. However, the existing super-resolution methods require high-dimensional matrix operations in the case of wide-field imaging, which makes it difficult to satisfy the requirements of real-time signal processing. To solve this problem, this paper introduces an improved Poisson distribution-based maximum likelihood (IPML) method by adding an adaptive iterative acceleration factor to effectively improve the algorithm convergence speed without introducing high-dimensional matrix operations. Furthermore, a GPU-based parallel processing architecture is proposed through the multithreading characteristics of the computing platform, and a cooperative CPU–GPU working model is constructed. This can realize the parallel optimization of the echo reception, preprocessing, and super-resolution processing. We verify that the proposed parallel super-resolution method can significantly improve the computational efficiency without sacrificing performance, using a real dataset. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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31 pages, 640 KiB  
Article
Joint Detection, Tracking, and Classification of Multiple Extended Objects Based on the JDTC-PMBM-GGIW Filter
by Yuansheng Li, Ping Wei, Mingyi You, Yifan Wei and Huaguo Zhang
Remote Sens. 2023, 15(4), 887; https://doi.org/10.3390/rs15040887 - 5 Feb 2023
Cited by 5 | Viewed by 1562
Abstract
This paper focuses on the problem of joint detection, tracking, and classification (JDTC) for multiple extended objects (EOs) within a Poisson multi-Bernoulli (MB) mixture (PMBM) filter, where an EO is described as an ellipse, and the ellipse is modeled by a random matrix. [...] Read more.
This paper focuses on the problem of joint detection, tracking, and classification (JDTC) for multiple extended objects (EOs) within a Poisson multi-Bernoulli (MB) mixture (PMBM) filter, where an EO is described as an ellipse, and the ellipse is modeled by a random matrix. The EOs are classified according to the size information of the ellipse. Usually, detection, tracking, and classification are processed step-by-step. However, step-by-step processing ignores the coupling relationship between detection, tracking, and classification, resulting in information loss. In fact, detection, tracking, and classification affect each other, and JDTC is expected to be beneficial for achieving better overall performance. In the multi-target tracking problem based on RFS, the overall performance of the PMBM filter satisfying the conjugate priors has been verified to be superior to other filters. Specifically, the PMBM filter propagates multiple MB simultaneously during iterative updates and model the distribution of hitherto undetected EOs. At present, the PMBM filter is only applied to multiple extended objects tracking problem. Therefore, we consider using the PMBM filter to solve the JDTC problem of multiple EOs and further improve JDTC performance. Furthermore, the closed-form implementation based on the product of a gamma Gaussian inverse Wishart (GGIW) and class probability mass function (PMF) is proposed. The details of parameters calculation in the implementation process and the derivation of class PMF are presented in this paper. Simulation experiments verify that the proposed algorithm, named the JDTC-PMBM-GGIW filter, performs well in comparison to the existing JDTC strategies for multiple extended objects. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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15 pages, 1301 KiB  
Communication
Cooperated Moving Target Detection Approach for PA-FDA Dual-Mode Radar in Range-Ambiguous Clutter
by Zhixin Liu, Shengqi Zhu, Jingwei Xu, Lan Lan, Xiongpeng He and Ximin Li
Remote Sens. 2023, 15(3), 692; https://doi.org/10.3390/rs15030692 - 24 Jan 2023
Viewed by 1233
Abstract
This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial [...] Read more.
This paper proposes a cooperated range ambiguous clutter suppression method for moving target detection in the background of range-ambiguous clutter with a phased array (PA)–frequency diverse array (FDA) dual-mode radar. With the FDA mode, the range-ambiguous clutters are discriminated in the transmit spatial frequency domain, and thus the clutter covariance matrixes (CCMs) corresponding to unambiguous and ambiguous regions can be independently estimated. Therefore, the enhanced CCM can be reconstructed by using a linear combination of these distinguished CCMs from different range regions. With the PA mode, the enhanced CCM is applied, thus taking advantages of its high beampattern gain as well as alleviating the range ambiguous clutter suppression problem. Simulation results are presented to verify the effectiveness of the proposed method in range-ambiguous clutter scenarios. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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17 pages, 492 KiB  
Article
Direct Target Joint Detection and Tracking Based on Passive Multi-Static Radar
by Yiqi Chen, Ping Wei, Huaguo Zhang, Mingyi You and Wanchun Li
Remote Sens. 2023, 15(3), 624; https://doi.org/10.3390/rs15030624 - 20 Jan 2023
Cited by 1 | Viewed by 1362
Abstract
Traditional target tracking is carried out based on the point measurements extracted from the radar resolution cells. This is not suitable for situations of low signal-to-noise ratio (SNR). In this paper, we aim to investigate the problem of the joint detection and tracking [...] Read more.
Traditional target tracking is carried out based on the point measurements extracted from the radar resolution cells. This is not suitable for situations of low signal-to-noise ratio (SNR). In this paper, we aim to investigate the problem of the joint detection and tracking (JDT) of a target by directly using the received signals of passive multi-static radar without feeding the signals to matched filters. To this end, a novel likelihood function is proposed exploiting the statistical properties of coherent processing between the reference and surveillance signals. With such a likelihood function, the particle Bernoulli filter is employed to perform direct JDT (DJDT) of the target. A remarkable feature of the proposed method is that it is able to achieve satisfactory performance when the SNR of received signals is low. Furthermore, the proposed method cannot only achieve the existence and kinematic state of the target, but also the time-varying SNR of each receiver, which serves as an important input for sensor adjustment. The performance of the proposed method is verified via simulations. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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15 pages, 6694 KiB  
Article
Ship Recognition for SAR Scene Images under Imbalance Data
by Ronghui Zhan and Zongyong Cui
Remote Sens. 2022, 14(24), 6294; https://doi.org/10.3390/rs14246294 - 12 Dec 2022
Cited by 3 | Viewed by 1601
Abstract
Synthetic aperture radar (SAR) ship recognition can obtain location and class information from SAR scene images, which is important in military and civilian fields, and has turned into a very important research focus recently. Limited by data conditions, the current research mainly includes [...] Read more.
Synthetic aperture radar (SAR) ship recognition can obtain location and class information from SAR scene images, which is important in military and civilian fields, and has turned into a very important research focus recently. Limited by data conditions, the current research mainly includes two aspects: ship detection in SAR scene images and ship classification in SAR slice images. These two parts are not yet integrated, but it is necessary to integrate detection and classification in practical applications, although it will cause an imbalance of training samples for different classes. To solve these problems, this paper proposes a ship recognition method on the basis of a deep network to detect and classify ship targets in SAR scene images under imbalance data. First, RetinaNet is used as the backbone network of the method in this paper for the integration of ship detection and classification in SAR scene images. Then, taking into account the issue that there are high similarities among various SAR ship classes, the squeeze-and-excitation (SE) module is introduced for amplifying the difference features as well as reducing the similarity features. Finally, considering the problem of class imbalance in ship target recognition in SAR scene images, a loss function, the central focal loss (CEFL), based on depth feature aggregation is constructed to reduce the differences within classes. Based on the dataset from OpenSARShip and Sentinel-1, the results of the experiment suggest that the the proposed method is feasible and the accuracy of the proposed method is improved by 3.9 percentage points compared with the traditional RetinaNet. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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19 pages, 1961 KiB  
Article
Absorption Pruning of Deep Neural Network for Object Detection in Remote Sensing Imagery
by Jielei Wang, Zongyong Cui, Zhipeng Zang, Xiangjie Meng and Zongjie Cao
Remote Sens. 2022, 14(24), 6245; https://doi.org/10.3390/rs14246245 - 9 Dec 2022
Cited by 3 | Viewed by 1577
Abstract
In recent years, deep convolutional neural networks (DCNNs) have been widely used for object detection tasks in remote sensing images. However, the over-parametrization problem of DCNNs hinders their application in resource-constrained remote sensing devices. In order to solve this problem, we propose a [...] Read more.
In recent years, deep convolutional neural networks (DCNNs) have been widely used for object detection tasks in remote sensing images. However, the over-parametrization problem of DCNNs hinders their application in resource-constrained remote sensing devices. In order to solve this problem, we propose a network pruning method (named absorption pruning) to compress the remote sensing object detection network. Unlike the classical iterative three-stage pruning pipeline used in existing methods, absorption pruning is designed as a four-stage pruning pipeline that only needs to be executed once, which differentiates it from existing methods. Furthermore, the absorption pruning no longer identifies unimportant filters, as in existing pruning methods, but instead selects filters that are easy to learn. In addition, we design a method for pruning ratio adjustment based on the object characteristics in remote sensing images, which can help absorption pruning to better compress deep neural networks for remote sensing image processing. The experimental results on two typical remote sensing data sets—SSDD and RSOD—demonstrate that the absorption pruning method not only can remove 60% of the filter parameters from CenterNet101 harmlessly but also eliminate the over-fitting problem of the pre-trained network. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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21 pages, 32807 KiB  
Article
Fast Line Segment Detection and Large Scene Airport Detection for PolSAR
by Daochang Wang, Qi Liu, Qiang Yin and Fei Ma
Remote Sens. 2022, 14(22), 5842; https://doi.org/10.3390/rs14225842 - 18 Nov 2022
Cited by 5 | Viewed by 1664
Abstract
In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of [...] Read more.
In this paper, we propose a fast Line Segment Detection algorithm for Polarimetric synthetic aperture radar (PolSAR) data (PLSD). We introduce the Constant False Alarm Rate (CFAR) edge detector to obtain the gradient map of the PolSAR image, which tests the equality of the covariance matrix using the test statistic in the complex Wishart distribution. A new filter configuration is applied here to save time. Then, the Statistical Region Merging (SRM) framework is utilized for the generation of line-support regions. As one of our main contributions, we propose a new Statistical Region Merging algorithm based on gradient Strength and Direction (SRMSD). It determines the merging predicate with consideration of both gradient strength and gradient direction. For the merging order, we set it by bucket sort based on the gradient strength. Furthermore, the pixels are restricted to belong to a unique region, making the algorithm linear in time cost. Finally, based on Markov chains and a contrario approach, the false alarm control of line segments is implemented. Moreover, a large scene airport detection method is designed based on the proposed line segment detection algorithm and scattering characteristics. The effectiveness and applicability of the two methods are demonstrated with PolSAR data provided by UAVSAR. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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22 pages, 20301 KiB  
Article
UltraHi-PrNet: An Ultra-High Precision Deep Learning Network for Dense Multi-Scale Target Detection in SAR Images
by Zheng Zhou, Zongyong Cui, Zhipeng Zang, Xiangjie Meng, Zongjie Cao and Jianyu Yang
Remote Sens. 2022, 14(21), 5596; https://doi.org/10.3390/rs14215596 - 6 Nov 2022
Cited by 7 | Viewed by 1910
Abstract
Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences [...] Read more.
Multi-scale target detection in synthetic aperture radar (SAR) images is one of the key techniques of SAR image interpretation, which is widely used in national defense and security. However, multi-scale targets include several types. For example, targets with similar-scale, large-scale, and ultra-large-scale differences coexist in SAR images. In particular, it is difficult for existing target detection methods to detect both ultra-large-scale targets and ultra-small-scale targets in SAR images, resulting in poor detection results for these two types of targets. To solve these problems, this paper proposes an ultra-high precision deep learning network (UltraHi-PrNet) to detect dense multi-scale targets. Firstly, a novel scale transfer layer is constructed to transfer the features of targets of different scales from bottom networks to top networks, ensuring that the features of ultra-small-scale, small-scale, and medium-scale targets in SAR images can be extracted more easily. Then, a novel scale expansion layer is constructed to increase the range of the receptive field of feature extraction without increasing the feature resolution, ensuring that the features of large-scale and ultra-large-scale targets in SAR images can be extracted more easily. Next, the scale expansion layers with different expansion rates are densely connected to different stages of the backbone network, and the features of the target with ultra-large-scale differences are extracted. Finally, the classification and regression of targets were achieved based on Faster R-CNN. Based on the SAR ship detection dataset (SSDD), AIR-SARShip-1.0, high-resolution SAR ship detection dataset-2.0 (high-resolution SSDD-2.0), the SAR-ship-dataset, and the Gaofen-3 airport dataset, the experimental results showed that this method can detect similar-scale, large-scale, and ultra-large-scale targets more easily. At the same time, compared with other advanced SAR target detection methods, the proposed method can achieve higher accuracy. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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19 pages, 6546 KiB  
Article
An Estimation and Compensation Method for Motion Trajectory Error in Bistatic SAR
by Yi Li, Wenchao Li, Junjie Wu, Zhichao Sun, Huarui Sun and Jianyu Yang
Remote Sens. 2022, 14(21), 5522; https://doi.org/10.3390/rs14215522 - 2 Nov 2022
Viewed by 1269
Abstract
Bistatic synthetic aperture radar (BiSAR) has drawn increasing attention in recent studies benefiting from its ability for forward-looking imaging, its capability of receiver radio silence and its resistance to jamming. However, the motion trajectory error compensation of BiSAR is a challenging task due [...] Read more.
Bistatic synthetic aperture radar (BiSAR) has drawn increasing attention in recent studies benefiting from its ability for forward-looking imaging, its capability of receiver radio silence and its resistance to jamming. However, the motion trajectory error compensation of BiSAR is a challenging task due to multiple error sources and complex effects. In this paper, an estimation and compensation method for three-dimensional (3D) motion trajectory error of BiSAR is proposed. In this method, the Doppler error of multiple scattering points is estimated firstly by using the time–frequency analysis method. Next, a local autofocus process is introduced to improve the Doppler error estimation accuracy. Then, the 3D trajectory error of BiSAR is estimated by solving a series of linear equations of the trajectory error and the Doppler error with the least squares method, and a well-focused BiSAR image is produced by using the corrected 3D trajectories. Finally, simulation and experiment results are presented to demonstrate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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20 pages, 5937 KiB  
Article
An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
by Yongkang Li, Tianyu Huo and Cuiqian Cao
Remote Sens. 2022, 14(21), 5453; https://doi.org/10.3390/rs14215453 - 30 Oct 2022
Cited by 2 | Viewed by 1338
Abstract
Medium-Earth-orbit (MEO) synthetic aperture radar (SAR) has the advantages of short revisit time and wide coverage, and thus is a potential tool for implementing ground moving target indication (GMTI) tasks. In the paper, aiming at MEO SAR’s problems of low signal-to-noise ratio and [...] Read more.
Medium-Earth-orbit (MEO) synthetic aperture radar (SAR) has the advantages of short revisit time and wide coverage, and thus is a potential tool for implementing ground moving target indication (GMTI) tasks. In the paper, aiming at MEO SAR’s problems of low signal-to-noise ratio and limited computation resource, an efficient imaging method is proposed for MEO multichannel SAR-GMTI systems with relatively low resolution. The proposed imaging method is designed with the consideration of both static scenes and ground moving targets, and it can simultaneously correct the range cell migrations of static scenes and multiple moving targets of no Doppler ambiguity. It needs only four Fourier transforms and twice phase multiplications, and thus is computationally efficient. Moreover, moving targets’ signal characteristics, including the azimuth and range displacements and along-track interferometric phase, in the SAR image obtained by the proposed imaging method are figured out. Experimental results validate the proposed imaging method and the theoretical analyses. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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36 pages, 19656 KiB  
Article
Cognitive Sparse Imaging Method for MIMO Radar under Wideband Interference
by Weike Feng, Pengcheng Wan, Xiaowei Hu, Yiduo Guo and Hangui Zhu
Remote Sens. 2022, 14(21), 5294; https://doi.org/10.3390/rs14215294 - 22 Oct 2022
Viewed by 1023
Abstract
Target three-dimensional (3D) high-resolution imaging via multiple-input multiple-output (MIMO) radar may suffer from a heavy sampling burden and complicated radio frequency interferences. Considering a collocated two-dimensional wideband MIMO radar under dynamic wideband interference (WBI), this paper proposes a cognitive method to achieve a [...] Read more.
Target three-dimensional (3D) high-resolution imaging via multiple-input multiple-output (MIMO) radar may suffer from a heavy sampling burden and complicated radio frequency interferences. Considering a collocated two-dimensional wideband MIMO radar under dynamic wideband interference (WBI), this paper proposes a cognitive method to achieve a 3D high-resolution target image with a reduced sampling cost. Firstly, based on the known knowledge of the target and WBI, provided by previous measurements, optimal sparse sampling in the 3D signal domain is conducted to reduce the number of sub-pulses and transceiving antennas by solving an optimization problem. Then, the detection and removal of the interfered signal components are conducted to provide the WBI information for following measurements and the interference-free signal cube for the target imaging process. Finally, by using the tensor-based smoothed L0 algorithm, the 3D high-resolution image of the target is obtained, providing the target information for the next measurement. Based on these three steps, a cognitive sparse imaging loop is formed for MIMO radar under WBI situations. The simulation and experiment results demonstrate the effectiveness and advantage of the proposed methods. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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21 pages, 5694 KiB  
Article
Extension and Evaluation of SSC for Removing Wideband RFI in SLC SAR Images
by Bingxu Chen, Zongsen Lv, Pingping Lu, Gaofeng Shu, Yabo Huang and Ning Li
Remote Sens. 2022, 14(17), 4294; https://doi.org/10.3390/rs14174294 - 31 Aug 2022
Cited by 6 | Viewed by 1452
Abstract
Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI [...] Read more.
Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI extraction and mitigation by subtracting between sub-images, which are robust and efficient for engineering applications. In the past, the traditional SSC was often applied to narrowband interference (NBI) mitigation. However, when it was used for wideband interference (WBI) mitigation, it would cause the mitigated image to lose much of its useful information. In contrast, this paper proposes an improved SSC method based on successive cancellation and data accumulation (SSC-SCDA) for WBI mitigation. First, the fast Fourier transform (FFT) is used to characterize the SAR SLC data in the frequency domain, and the average range spectrum algorithm is used to detect whether there are interference components in the SAR SLC data. Then, according to the carrier frequency and bandwidth of the RFI in the frequency domain, the subbands are divided, and a cancellation strategy is formulated. Finally, based on the successive cancellation and data accumulation technology, WBIs can be removed by using only a small percentage of the clean subbands. Based on the simulated experiments, the interference mitigation performance of the proposed method is analyzed when the interference-to-signal bandwidth ratio (ISBR) varies from 20% to 80% under different signal-to-interference-to-noise ratios (SINR). The experimental results based on WBI-contaminated European Space Agency (ESA) Sentinel-1A SAR SLC data demonstrate the effectiveness of the proposed method in WBI mitigation. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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Graphical abstract

19 pages, 9180 KiB  
Article
Targets’ Radial and Tangential Velocities Estimation Based on Vortex Electromagnetic Waves
by Caipin Li, Shengyuan Li, Dong You, Wencan Peng, Jinwei Li, Yu Li, Qiang Li and Zhanye Chen
Remote Sens. 2022, 14(16), 3861; https://doi.org/10.3390/rs14163861 - 9 Aug 2022
Cited by 4 | Viewed by 1524
Abstract
The orbital angular momentum (OAM) of a vortex electromagnetic wave (VEW) has gained attention as a newly explored information carrier. OAM modes provide vortex azimuth resolution, which is a new degree of freedom (DOF) in radar application. Due to the special characteristics of [...] Read more.
The orbital angular momentum (OAM) of a vortex electromagnetic wave (VEW) has gained attention as a newly explored information carrier. OAM modes provide vortex azimuth resolution, which is a new degree of freedom (DOF) in radar application. Due to the special characteristics of the vortex azimuth domain, VEW shares compound Doppler information of two-dimensional (2D) speed. This paper proposes a 2D target velocity estimation method for VEW radar. The Doppler effect of VEW is first analyzed. Based on the relativity of tangential speed and OAM mode, a pulse-by-pulse OAM mode-changing strategy is designed. Then, a modified Radon–Fourier transformation (RFT) is proposed to estimate the compound Doppler frequency while range migration is compensated. In addition, decoupling and ambiguity-solving procedures are applied to the compound Doppler frequency estimation to obtain tangential and radial speed estimations separately. According to the simulation analyses, the effectiveness of the proposed method is verified. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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17 pages, 5617 KiB  
Article
A Novel Method for SAR Ship Detection Based on Eigensubspace Projection
by Gaofeng Shu, Jiahui Chang, Jing Lu, Qing Wang and Ning Li
Remote Sens. 2022, 14(14), 3441; https://doi.org/10.3390/rs14143441 - 18 Jul 2022
Cited by 3 | Viewed by 1686
Abstract
Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, [...] Read more.
Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, in complex scenes, ships are easily submerged in sea clutter, which cause missed detection. Due to this, strong sidelobes in SAR images generate false targets and reduce the detection accuracy. To solve these problems, a ship detection method based on eigensubspace projection (ESSP) in SAR images is proposed. First, the image is reconstructed into a new observation matrix along the azimuth direction, and the phase space matrix of the reconstructed image is constructed by using the Hankel characteristic, which preliminarily determines the approximate position of the ship. Then, the autocorrelation matrix of the reconstructed image is decomposed by eigenvalue decomposition (EVD). According to the size of the eigenvalues, the corresponding eigenvectors are divided into two parts, which constitute the basis of the ship subspace and the clutter subspace. Finally, the original image is projected into the ship subspace, and the ship data in the ship subspace are rearranged to obtain the precise position of the ship with significantly suppressed clutter. To verify the effectiveness of the proposed method, the ESSP method is compared with other detection methods on four images at different sea conditions. The results show that the detection accuracy of the ESSP method reaches 89.87% in complex scenes. Compared with other methods, the proposed method can extract ship targets from sea clutter more accurately and reduce the number of false alarms, which has obvious advantages in terms of detection accuracy and timeliness. Full article
(This article belongs to the Special Issue Applications of Synthetic Aperture Radar to Target Detection and Tracking)
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