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Small or Moving Target Detection with Advanced Radar System
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Small or Moving Target Detection with Advanced Radar System

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 (15 October 2022) | Viewed by 41170

Special Issue Editors

Prof. Dr. Jingwei Xu

E-Mail Website
Guest Editor
National Lab of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: waveform diversity; array signal processing; space-time adaptive processing
Special Issues, Collections and Topics in MDPI journals
Dr. Keqing Duan

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: space–time adaptive processing; array signal processing; sparse recovery
Dr. Weijian Liu

E-Mail Website
Guest Editor
Wuhan Electronic Information Institute, Wuhan 410039, China
Interests: multichannel signal detection; statistical and array signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiongpeng He

E-Mail Website
Guest Editor
National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, China
Interests: ground moving target detection; clutter suppression; waveform diverse array

Special Issue Information

Dear Colleagues,

With the development of many kinds of unmanned aerial vehicles and aircrafts, sensor systems have encountered increasing challenges in small and moving target detection. With ground and sea clutter backgrounds, it is difficult to distinguish slow-moving targets using spaceborne and/or airborne radar systems. Additionally, the increasing electromagnetic jamming environment makes moving target detection task even harder. This issue discusses moving target detection in a dense jamming environment and/or with strong clutter background with spaceborne/airborne early warning and surveillance radar systems. Interesting topics include ground/sea clutter suppression, anti-jamming even those from the mainlobe region, group target detection and localization, and radar and communication co-existence. It is important and meaningful to recast the array signal processing theory as well as advanced techniques to solve new problems and meet new application demands. Possible solutions include advanced signal processing techniques by exploiting the characteristic of moving targets, clutter and jamming, sophisticated array signal processing and algorithms by optimally choosing the parameters or structure of the algorithms, novel design of the array radar system by introducing frequency diversity and waveform diversity, cooperated signal processing with multi-static radar systems, etc.

Clutter and jamming suppression technique and target detection technique are important for remote sensing.

Dr. Jingwei Xu
Dr. Keqing Duan
Dr. Weijian Liu
Dr. Xiongpeng He
Guest Editors

Manuscript Submission Information

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Keywords

  • moving target detection
  • clutter suppression
  • jamming suppression
  • spaceborne/airborne radar
  • array signal processing
  • waveform diversity

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Published Papers (20 papers)

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Research

26 pages, 11097 KiB  
Article
ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images
by Canming Yao, Pengfei Xie, Lei Zhang and Yuyuan Fang
Remote Sens. 2022, 14(23), 6058; https://doi.org/10.3390/rs14236058 - 29 Nov 2022
Cited by 7 | Viewed by 2276
Abstract
Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on [...] Read more.
Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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18 pages, 747 KiB  
Article
Autoencoder Neural Network-Based STAP Algorithm for Airborne Radar with Inadequate Training Samples
by Jing Liu, Guisheng Liao, Jingwei Xu, Shengqi Zhu, Filbert H. Juwono and Cao Zeng
Remote Sens. 2022, 14(23), 6021; https://doi.org/10.3390/rs14236021 - 28 Nov 2022
Cited by 4 | Viewed by 1593
Abstract
Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the [...] Read more.
Clutter suppression is a key problem for airborne radar, and space-time adaptive processing (STAP) is a core technology for clutter suppression and moving target detection. However, in practical applications, the non-uniform time-varying environments including clutter range dependence for non-side-looking radar lead to the training samples being unable to satisfy the sample requirements of STAP that they should be independent identical distributed (IID) and that their number should be greater than twice the system’s degree of freedom (DOF). The lack of sufficient IID training samples causes difficulty in the convergence of STAP and further results in a serious degeneration of performance. To overcome this problem, this paper proposes a novel autoencoder neural network for clutter suppression with a unique matrix designed to be decoded and encoded. The main challenges are improving the accuracy of the estimation of the clutter-plus-noise covariance matrix (CNCM) for STAP convergence, designing the form of the data input to the network, and making the network successfully explored to the improvement of CNCM. For these challenges, the main proposed solutions include designing a unique matrix with a certain dimension and a series of covariance data selections and matrix transformations. Consequently, the proposed method compresses and retains the characteristics of the covariances, and abandons the deviations caused by the non-uniformity and the deficiency of training samples. Specifically, the proposed method firstly develops a unique matrix whose dimension is less than half of the DOF, meanwhile, it is based on a processing of the selected clutter-plus-noise covariances. Then, an autoencoder neural network with l2 regularization and the sparsity regularization is proposed for the unique matrix to be decoded and encoded. The training of the proposed autoencoder can be achieved by reducing the total loss function with the gradient descent iterations. Finally, an inverted processing for the autoencoder output is designed for the reconstruct ion of the clutter-plus-noise covariances. Simulation results are used to verify the effectiveness and advantages of the proposed method. It performs obviously superior clutter suppression for both side-looking and non-side-looking radars with strong clutter, and can deal with the insufficient and the non-uniform training samples. For these conditions, the proposed method provides the relatively narrowest and deepest IF notch. Furthermore, on average it improves the improvement factor (IF) by 10 dB more than the ADC, DW, JDL, and original STAP methods. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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15 pages, 9452 KiB  
Communication
Mainlobe Deceptive Jammer Suppression Based on Quadratic Phase Coding in FDA-MIMO Radar
by Yiqun Zhang, Guisheng Liao, Jingwei Xu and Lan Lan
Remote Sens. 2022, 14(22), 5831; https://doi.org/10.3390/rs14225831 - 17 Nov 2022
Cited by 8 | Viewed by 1519
Abstract
In this paper, the problem of mainlobe deceptive jammer suppression is solved with the frequency diversity array-multiple-input multiple-output (FDA-MIMO) radar system. At the modeling stage, based on the FDA-MIMO radar, a quadratic phase code (QPC) is applied along the slow time dimension in [...] Read more.
In this paper, the problem of mainlobe deceptive jammer suppression is solved with the frequency diversity array-multiple-input multiple-output (FDA-MIMO) radar system. At the modeling stage, based on the FDA-MIMO radar, a quadratic phase code (QPC) is applied along the slow time dimension in the transmit array. In the receiver, after decoding and principal range compensation, the true and false targets that are generated in an identical angle, can be discriminated in the joint transmit-receive-Doppler frequency domain. Particularly, the false targets are equivalently moved from the mainlobe to the sidelobes in the transmit spatial frequency domain. Then, by performing the data-dependent transmit-receive-Doppler three-dimensional beamforming, the false targets are suppressed owing to Doppler and range mismatches. Moreover, by moving the jammers to nulls in the Doppler frequency domain, the capability in terms of the maximum number of suppressible jammers can be strengthened with an appropriate coding coefficient and frequency increment. Numerical results can certify the suppression capability of the QPC-FDA-MIMO radar. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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24 pages, 8956 KiB  
Article
Research on Multi-Domain Dimensionality Reduction Joint Adaptive Processing Method for Range Ambiguous Clutter of FDA-Phase-MIMO Space-Based Early Warning Radar
by Tianfu Zhang, Zhihao Wang, Mengdao Xing, Shuangxi Zhang and Yongliang Wang
Remote Sens. 2022, 14(21), 5536; https://doi.org/10.3390/rs14215536 - 2 Nov 2022
Cited by 9 | Viewed by 1983
Abstract
The ground and sea clutter received by space-based early warning radar (SBEWR) has severely range ambiguous characteristics due to its platform location, and the non-stationary factor caused by Earth’s rotation makes the received clutter at different range ambiguous positions seriously broaden in the [...] Read more.
The ground and sea clutter received by space-based early warning radar (SBEWR) has severely range ambiguous characteristics due to its platform location, and the non-stationary factor caused by Earth’s rotation makes the received clutter at different range ambiguous positions seriously broaden in the Doppler dimension. The complex clutter suppression performance of SBEWR obtained by traditional method is degraded significantly. To solve this problem and achieve better clutter suppression performance, a novel multi-domain adaptive processing method for clutter suppression is proposed in this paper. The proposed method introduced a range related signal processing domain based on conventional space–time domain by using frequency diverse array phase multiple-input multiple-output (FDA-Phase-MIMO) radar. In addition, a novel multi-domain joint dimensionality reduction structure was designed. The novel multi-domain joint adaptive processing using the proposed dimensionality reduction structure could not only obtain great clutter suppression performance of SBEWR, but also minimize the requirement of the number of selected auxiliary channels. Simulation examples show the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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17 pages, 2126 KiB  
Communication
End-to-End Moving Target Indication for Airborne Radar Using Deep Learning
by Yao Gu, Jianxin Wu, Yuyuan Fang, Lei Zhang and Qiang Zhang
Remote Sens. 2022, 14(21), 5354; https://doi.org/10.3390/rs14215354 - 26 Oct 2022
Cited by 6 | Viewed by 2509
Abstract
Moving target indication (MTI) based on space–time adaptive processing (STAP) has been widely used in airborne radar due to its ability for clutter suppression performance. However, the existing MTI methods suffer from the problems of insufficient training samples and low detection probability in [...] Read more.
Moving target indication (MTI) based on space–time adaptive processing (STAP) has been widely used in airborne radar due to its ability for clutter suppression performance. However, the existing MTI methods suffer from the problems of insufficient training samples and low detection probability in a non-homogeneous clutter environment. To address these issues, this paper proposes a novel deep learning framework to improve target indication capability. First, combined with the problems of target indication caused by the non-homogeneous clutter, the clutter-plus-target training dataset was modeled by simulation, where various non-ideal factors, such as aircraft crabbing, array errors and internal clutter motion (ICM), were considered. The dataset considers various realistic situations, making the proposed method more robust. Then, a five-layer two-dimensional convolutional neural network (D2CNN) was designed and applied to learn the clutter and target characteristics distribution. The proposed D2CNN can predict the target with a high resolution to implement an end-to-end moving target indication (ETE-MTI) with a higher detection accuracy. In this D2CNN, the input was obtained by the clutter-plus-target angle-Doppler spectrum with a low-resolution estimated only by a few samples. The label was given by the target angle-Doppler spectrum with a high-resolution obtained by the target’s exact angle and Doppler. Thirdly, the proposed method used a few samples to improve the target indication and detection probability, which solved the problem of insufficient samples in the non-homogeneous clutter environments. To elaborate, the proposed method directly implements ETE-MTI without the support of the conventional STAP algorithm to suppress the clutter. The results verify the validity and the robustness of the proposed ETE-MTI with a few samples in the non-homogeneous and low signal-to-clutter ratio (SCR) environments. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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Graphical abstract

11 pages, 3165 KiB  
Communication
Adaptive Robust Radar Target Detector Based on Gradient Test
by Zeyu Wang, Jun Liu, Hongmeng Chen and Wei Yang
Remote Sens. 2022, 14(20), 5236; https://doi.org/10.3390/rs14205236 - 20 Oct 2022
Cited by 3 | Viewed by 1675
Abstract
The exact knowledge of the signal steering vector is not always known, which may result in detection performance degradation when a signal mismatch occurs. In this paper, we discuss the problem of designing a robust radar target detector in the background of Gaussian [...] Read more.
The exact knowledge of the signal steering vector is not always known, which may result in detection performance degradation when a signal mismatch occurs. In this paper, we discuss the problem of designing a robust radar target detector in the background of Gaussian noise whose covariance matrix is unknown. To improve robustness to mismatched signals, a random perturbation that follows the complex normal distribution is added under the alternative hypothesis. Since traditional detectors that divide complex parameters into real parts and imaginary parts are sometimes difficult to obtain, a new robust, complex parameter gradient test is derived directly from the complex data. Moreover, the CFAR property of the new detector is proven. The performance assessment indicates that the gradient detector exhibits suitable robustness to the mismatched signals. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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Graphical abstract

23 pages, 4141 KiB  
Article
WCA-Based Low-PSLL and Wide-Nulling Beampattern Synthesis for Radar Applications
by Yanhong Xu, Dongyun Wang, Anyi Wang and Yan Yan
Remote Sens. 2022, 14(17), 4204; https://doi.org/10.3390/rs14174204 - 26 Aug 2022
Viewed by 1546
Abstract
There are many unavoidable array errors in practical scenarios, which would drastically increase the sidelobe level (SLL) and distort the performance of radar systems accordingly. In this paper, an effective beampattern synthesis approach is proposed to realize a low peak sidelobe level (PSLL) [...] Read more.
There are many unavoidable array errors in practical scenarios, which would drastically increase the sidelobe level (SLL) and distort the performance of radar systems accordingly. In this paper, an effective beampattern synthesis approach is proposed to realize a low peak sidelobe level (PSLL) and wide-nulling in the presence of array errors, thus improving the consequent performance of the radar. In particular, the covariance matrix of the sidelobe region (CMSR) is incorporated into the optimization. Considering the randomness of array errors, the statistical mean method is adopted to obtain a more accurate calculation of the CMSR in the presence of array errors based on a Monte Carlo trial. To efficiently and effectively solve the optimization problem, an advanced metaheuristic algorithm, i.e., the water cycle algorithm (WCA), is adopted when seeking the corresponding optimal weight vectors. Numerical results are provided and discussed to demonstrate the effectiveness of the proposed approach including the results based on a wideband linearly spaced magneto-electric (ME) dipole array. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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20 pages, 9257 KiB  
Article
Optimal Configuration of Array Elements for Hybrid Distributed PA-MIMO Radar System Based on Target Detection
by Cheng Qi, Junwei Xie, Haowei Zhang, Zihang Ding and Xiao Yang
Remote Sens. 2022, 14(17), 4129; https://doi.org/10.3390/rs14174129 - 23 Aug 2022
Cited by 4 | Viewed by 2025
Abstract
This paper establishes a hybrid distributed phased array multiple-input multiple-output (PA-MIMO) radar system model to improve the target detection performance by combining coherent processing gain and spatial diversity gain. First, the radar system signal model and array space configuration model for the PA-MIMO [...] Read more.
This paper establishes a hybrid distributed phased array multiple-input multiple-output (PA-MIMO) radar system model to improve the target detection performance by combining coherent processing gain and spatial diversity gain. First, the radar system signal model and array space configuration model for the PA-MIMO radar are established. Then, a novel likelihood ratio test (LRT) detector is derived based on the Neyman–Pearson (NP) criterion in a fixed noise background. It can jointly optimize the coherent processing gain and spatial diversity gain of the system by implementing subarray level and array element level optimal configuration at both receiver and transmitter ends in a uniform blocking manner. On this basis, three typical optimization problems are discussed from three aspects, i.e., the detection probability, the effective radar range, and the radar system equipment volume. The approximate closed-form solutions of them are constructed and solved by the proposed quantum particle swarm optimization-based stochastic rounding (SR-QPSO) algorithm. Through the simulations, it is verified that the proposed optimal configuration of the hybrid distributed PA-MIMO radar system offers substantial improvements compared to the other typical radar systems, detection probability of 0.98, and an effective range of 1166.3 km, which significantly improves the detection performance. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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22 pages, 7723 KiB  
Article
Improved Dimension-Reduced Structures of 3D-STAP on Nonstationary Clutter Suppression for Space-Based Early Warning Radar
by Zhihao Wang, Wei Chen, Tianfu Zhang, Mengdao Xing and Yongliang Wang
Remote Sens. 2022, 14(16), 4011; https://doi.org/10.3390/rs14164011 - 18 Aug 2022
Cited by 11 | Viewed by 1713
Abstract
By introducing degrees of freedom (DOFs) in elevation, the elevation-azimuth-Doppler three-dimensional space–time adaptive processing (3D-STAP) methods have better performance when suppressing the nonstationary clutter caused by the Earth’s rotation in space-based early warning radar (SBEWR). However, the 3D-STAP methods use much more auxiliary [...] Read more.
By introducing degrees of freedom (DOFs) in elevation, the elevation-azimuth-Doppler three-dimensional space–time adaptive processing (3D-STAP) methods have better performance when suppressing the nonstationary clutter caused by the Earth’s rotation in space-based early warning radar (SBEWR). However, the 3D-STAP methods use much more auxiliary beams, leading to greater demands on the training samples and heavier computational burdens than the conventional STAP methods. To solve this problem, the ideas of sum–difference beams, generalized multiple beams and Doppler-domain localization are applied here, and three improved dimension-reduced structures of 3D-STAP are proposed in this article. After analyzing the characteristics and distribution of nonstationary clutter for SBEWR, we find that the demands for auxiliary beams are different in elevation, azimuth and Doppler dimension. In addition, the suggestion to choose the number of auxiliary beams in each dimension is given. Simulation experiments are conducted to verify the analysis and evaluate the performance of the proposed methods. The simulation results show that the proposed 3D-STAP methods have better performance and lower computational burdens than typical 3D-STAP methods. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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20 pages, 3313 KiB  
Article
Random Matrix Theory-Based Reduced-Dimension Space-Time Adaptive Processing under Finite Training Samples
by Di Song, Qi Feng, Shengyao Chen, Feng Xi and Zhong Liu
Remote Sens. 2022, 14(16), 3959; https://doi.org/10.3390/rs14163959 - 15 Aug 2022
Cited by 2 | Viewed by 1671
Abstract
Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, [...] Read more.
Space-time adaptive processing (STAP) is a fundamental topic in airborne radar applications due to its clutter suppression ability. Reduced-dimension (RD)-STAP can release the requirement of the number of training samples and reduce the computational load from traditional STAP, which attracts much attention. However, under the situation that training samples are severely deficient, RD-STAP will become poor like the traditional STAP. To enhance RD-STAP performance in such cases, this paper develops a novel RD-STAP algorithm using random matrix theory (RMT), RMT-RD-STAP. By minimizing the output clutter-plus-noise power, the estimate of the inversion of clutter plus noise covariance matrix (CNCM) can be obtained through optimally manipulating its eigenvalues, thus producing the optimal STAP weight vector. Specifically, the clutter-related eigenvalues are estimated according to the clutter-related sample eigenvalues via RMT, and the noise-related eigenvalue is optimally selected from the noise-related sample eigenvalues. It is found that RMT-RD-STAP significantly outperforms the RD-STAP algorithm when the RMB rule cannot be satisfied. Theoretical analyses and numerical results demonstrate the effectiveness and the performance advantages of the proposed RMT-RD-STAP algorithm. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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25 pages, 5278 KiB  
Article
On the Efficient Implementation of Sparse Bayesian Learning-Based STAP Algorithms
by Kun Liu, Tong Wang, Jianxin Wu, Cheng Liu and Weichen Cui
Remote Sens. 2022, 14(16), 3931; https://doi.org/10.3390/rs14163931 - 13 Aug 2022
Cited by 2 | Viewed by 1635
Abstract
Sparse Bayesian learning-based space–time adaptive processing (SBL-STAP) algorithms can achieve superior clutter suppression performance with limited training sample support in practical heterogeneous and non-stationary clutter environments. However, when the system has high degrees of freedom (DOFs), SBL-STAP algorithms suffer from high computational complexity, [...] Read more.
Sparse Bayesian learning-based space–time adaptive processing (SBL-STAP) algorithms can achieve superior clutter suppression performance with limited training sample support in practical heterogeneous and non-stationary clutter environments. However, when the system has high degrees of freedom (DOFs), SBL-STAP algorithms suffer from high computational complexity, since the large-scale matrix calculations and the inversion operations of large-scale covariance matrices are involved in the iterative process. In this article, we consider a computationally efficient implementation for SBL-STAP algorithms. The efficient implementation is based on the fact that the covariance matrices that need to be updated in the iterative process of the SBL-STAP algorithms have a Hermitian Toplitz-block-Toeplitz (HTBT) structure, with the result being that the inverse covariance matrix can be expressed in closed form by using a special case of the Gohberg–Semencul (G-S) formula. Based on the G-S-type factorization of the inverse covariance matrix and the structure of the used dictionary matrix, we can perform almost all operations in the SBL-STAP algorithms by 2-D FFT/IFFT. As a result, compared with the original SBL-STAP algorithms, even for moderate data sizes, the proposed algorithms can directly reduce the computational load by about two orders of magnitudes without any performance loss. Finally, simulation results validate the effectiveness of the proposed algorithms. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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Graphical abstract

18 pages, 3869 KiB  
Article
A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects
by Cheng Liu, Tong Wang, Kun Liu and Xinying Zhang
Remote Sens. 2022, 14(16), 3906; https://doi.org/10.3390/rs14163906 - 11 Aug 2022
Cited by 2 | Viewed by 1405
Abstract
Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots. However, once some portion of clutter is not located on the grids, i.e., off-grid problems, the performances of most SR-STAP algorithms degrade [...] Read more.
Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots. However, once some portion of clutter is not located on the grids, i.e., off-grid problems, the performances of most SR-STAP algorithms degrade significantly. Reducing the grid interval can mitigate off-grid effects, but brings strong column coherence of the dictionary, heavy computational load, and heavy storage load. A sparse Bayesian learning approach is proposed to mitigate the off-grid effects in the paper. The algorithm employs an efficient sequential addition and deletion of dictionary atoms to estimate the clutter subspace, which means that strong column coherence has no effect on the performance of the proposed algorithm. Besides, the proposed algorithm does not require much computational load and storage load. Off-grid effects can be mitigated with the proposed algorithm when the grid-interval is sufficiently small. The excellent performance of the novel algorithm is demonstrated on the simulated data. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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17 pages, 2589 KiB  
Communication
Transmit Antenna Selection and Power Allocation for Joint Multi-Target Localization and Discrimination in MIMO Radar with Distributed Antennas under Deception Jamming
by Zhengjie Li, Junwei Xie, Weijian Liu, Haowei Zhang and Houhong Xiang
Remote Sens. 2022, 14(16), 3904; https://doi.org/10.3390/rs14163904 - 11 Aug 2022
Cited by 6 | Viewed by 1688
Abstract
In this paper, with the aim of performing joint multi-target localization and discrimination tasks, a performance-driven resource allocation scheme is proposed. In the first, by establishing the signal model under deception jamming and utilizing the maximum likelihood (ML) estimator, the estimation information of [...] Read more.
In this paper, with the aim of performing joint multi-target localization and discrimination tasks, a performance-driven resource allocation scheme is proposed. In the first, by establishing the signal model under deception jamming and utilizing the maximum likelihood (ML) estimator, the estimation information of targets can be obtained. Secondly, the Cramer–Rao lower bound (CRLB) for the transmit antenna selection and power allocation is derived. Then, to fully utilize the difference in spatial distribution between true and false targets, a false target discriminator based on the CRLB of the distance deception parameter is utilized. By introducing the nondimensionalization mechanism, we build an optimal objective function of target localization error and discrimination probability. Subsequently, a joint multi-target localization and discrimination optimization model has been established, which is mathematically a non-smooth and non-convex problem. By introducing an auxiliary variable, we propose a three-step solution strategy for solving this problem. Simulation results demonstrate that the proposed algorithm can improve the performance of joint localization accuracy and discrimination ability (JLADA) by more than 30% compared with the algorithms only for localization or discrimination. Meanwhile, by utilizing the proposed algorithm, the composite indicators of JLADA can decrease more than 70% compared with the uniform allocation scheme. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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22 pages, 5029 KiB  
Article
DU-CG-STAP Method Based on Sparse Recovery and Unsupervised Learning for Airborne Radar Clutter Suppression
by Bo Zou, Xin Wang, Weike Feng, Hangui Zhu and Fuyu Lu
Remote Sens. 2022, 14(14), 3472; https://doi.org/10.3390/rs14143472 - 19 Jul 2022
Cited by 13 | Viewed by 2248
Abstract
With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, [...] Read more.
With a small number of training range cells, sparse recovery (SR)-based space–time adaptive processing (STAP) methods can help to suppress clutter and detect targets effectively for airborne radar. However, SR algorithms usually have problems of high computational complexity and parameter-setting difficulties. More importantly, non-ideal factors in practice will lead to the degraded clutter suppression performance of SR-STAP methods. Based on the idea of deep unfolding (DU), a space–time two-dimensional (2D)-decoupled SR network, namely 2DMA-Net, is constructed in this paper to achieve a fast clutter spectrum estimation without complicated parameter tuning. For 2DMA-Net, without using labeled data, a self-supervised training method based on raw radar data is implemented. Then, to filter out the interferences caused by non-ideal factors, a cycle-consistent adversarial network (CycleGAN) is used as the image enhancement process for the clutter spectrum obtained using 2DMA-Net. For CycleGAN, an unsupervised training method based on unpaired data is implemented. Finally, 2DMA-Net and CycleGAN are cascaded to achieve a fast and accurate estimation of the clutter spectrum, resulting in the DU-CG-STAP method with unsupervised learning, as demonstrated in this paper. The simulation results show that, compared to existing typical SR-STAP methods, the proposed method can simultaneously improve clutter suppression performance and reduce computational complexity. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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23 pages, 3890 KiB  
Article
Generalized Labeled Multi-Bernoulli Multi-Target Tracking with Doppler-Only Measurements
by Yun Zhu, Mahendra Mallick, Shuang Liang and Junkun Yan
Remote Sens. 2022, 14(13), 3131; https://doi.org/10.3390/rs14133131 - 29 Jun 2022
Cited by 10 | Viewed by 2001
Abstract
The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and [...] Read more.
The paper addresses the problem of tracking multiple targets with Doppler-only measurements in multi-sensor systems. It is well known that the observability of the target state measured using Doppler-only measurements is very poor, which makes it difficult to initialize the tracking target and produce the target trajectory in any tracking algorithm. Within the framework of random finite sets, we propose a novel constrained admissible region (CAR) based birth model that instantiates the birth distribution using Doppler-only measurements. By combining physics-based constraints in the unobservable subspace of the state space, the CAR based birth model can effectively reduce the ambiguity of the initial state. The CAR based birth model combines physics-based constraints in the unobservable subspace of the state space to reduce the ambiguity of the initial state. We implement the CAR based birth model with the generalized labeled multi-Bernoulli tracking filter to demonstrate the effectiveness of our proposed algorithm in Doppler-only tracking. The performance of the proposed approach is tested in two simulation scenarios in terms of the optimal subpattern assignment (OSPA) error, OSPA(2) 
(2)error, and computing efficiency. The simulation results demonstrate the superiority of the proposed approach. Compared to the approach taken by the state-of-the-art methods, the proposed approach can at most reduce the OSPA error by 58.77%, reduce the OSPA(2) error by 43.51%, and increase the computing efficiency by 9.56 times in the first scenario. In the second scenario, the OSPA error is reduced by 62.80%, the OSPA(2) (2)error is reduced by 43.65%, and the computing efficiency is increased by 2.61 times at most. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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22 pages, 1861 KiB  
Article
Dynamic Antenna Selection for Colocated MIMO Radar
by Gangsheng Zhang, Junwei Xie, Haowei Zhang, Zhengjie Li and Cheng Qi
Remote Sens. 2022, 14(12), 2912; https://doi.org/10.3390/rs14122912 - 18 Jun 2022
Cited by 6 | Viewed by 2413
Abstract
Antenna distribution plays an important role for the performance gain in multiple-input–multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each [...] Read more.
Antenna distribution plays an important role for the performance gain in multiple-input–multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramér–Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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18 pages, 6404 KiB  
Article
Composite Electromagnetic Scattering and High-Resolution SAR Imaging of Multiple Targets above Rough Surface
by Qingkuan Wang, Chuangming Tong, Ximin Li, Yijin Wang, Zhaolong Wang and Tong Wang
Remote Sens. 2022, 14(12), 2910; https://doi.org/10.3390/rs14122910 - 17 Jun 2022
Cited by 5 | Viewed by 2555
Abstract
Aiming at the high efficiency of composite electromagnetic scattering analysis and radar target detection and recognition utilizing high-range resolution profile (HRRP) characteristics and high-resolution synthetic aperture radar (SAR) images, a near-field modified iterative physical optics and facet-based two-scale model for analysis of composite [...] Read more.
Aiming at the high efficiency of composite electromagnetic scattering analysis and radar target detection and recognition utilizing high-range resolution profile (HRRP) characteristics and high-resolution synthetic aperture radar (SAR) images, a near-field modified iterative physical optics and facet-based two-scale model for analysis of composite electromagnetic scattering from multiple targets above rough surface have been presented. In this method, the coupling scattering of multiple targets is calculated by near-field iterative physical optics and the far-field scattering is calculated by the physical optics method. For the evaluation of the scattering of an electrically large sea surface, a slope cutoff probability distribution function is introduced in the two-scale model. Moreover, a fast imaging method is introduced based on the proposed hybrid electromagnetic scattering method. The numerical results show the effectiveness of the proposed method, which can generate backscattering data accurately and obtain high-resolution SAR images. It is concluded that the proposed method has the advantages of accurate computation and good recognition performance. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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20 pages, 4501 KiB  
Communication
PARAFAC Estimators for Coherent Targets in EMVS-MIMO Radar with Arbitrary Geometry
by Lei Zhang, Han Wang, Fang-Qing Wen and Jun-Peng Shi
Remote Sens. 2022, 14(12), 2905; https://doi.org/10.3390/rs14122905 - 17 Jun 2022
Cited by 6 | Viewed by 1786
Abstract
In the past few years, multiple-input multiple-output (MIMO) radar with electromagnetic vector sensor (EMVS) array, or called EMVS-MIMO radar, has attracted extensive attention in target detection. Unlike the traditional scalar sensor-based MIMO radar, an EMVS-MIMO radar can not only provides a two-dimensional (2D) [...] Read more.
In the past few years, multiple-input multiple-output (MIMO) radar with electromagnetic vector sensor (EMVS) array, or called EMVS-MIMO radar, has attracted extensive attention in target detection. Unlike the traditional scalar sensor-based MIMO radar, an EMVS-MIMO radar can not only provides a two-dimensional (2D) direction finding of the targets but also offers 2D polarization parameter estimation, which may be important for detecting weak targets. In this paper, we investigate into multiple parameter estimations for a bistatic EMVS-MIMO radar in the presence of coherent targets, whose transmitting EMVS and receiving EMVS are placed in an arbitrary topology. Three tensor-aware spatial smoothing estimators are introduced. The core of the proposed estimators is to de-correlate the coherent targets via the spatial smoothing technique and then formulate the covariance matrix into a third-order parallel factor (PARAFAC) tensor. After the PARAFAC decomposition of the tensor, the factor matrices can be obtained. Thereafter, the 2D direction finding can be accomplished via the normalized vector cross-product technique. Finally, the 2D polarization parameter can be estimated via the least squares method. Unlike the state-of-the-art PARAFAC estimator, the proposed estimators are suitable for arbitrary sensor geometries, and they are robust to coherent targets as well as sensor position errors. In addition, they have better estimation performance than the current matrix-based estimators. Moreover, they are computationally efficient than the current subspace methods, especially in the presence of a large-scale sensor array. In addition, the proposed estimators are analyzed in detail. Numerical experiments coincide with our theoretical findings. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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20 pages, 4554 KiB  
Article
A Cognitive Beamforming Method via Range-Doppler Map Features for Skywave Radar
by Zhenshuo Lei, Hui Chen, Zhaojian Zhang, Gaoqi Dou and Yongliang Wang
Remote Sens. 2022, 14(12), 2879; https://doi.org/10.3390/rs14122879 - 16 Jun 2022
Cited by 4 | Viewed by 1975
Abstract
For skywave over-the-horizon radar, beamforming techniques are often used to suppress airspace radio frequency interference because the high-frequency band is shared by many devices. To address the problems that the traditional beamforming method is not capable of recognizing the electromagnetic environment and that [...] Read more.
For skywave over-the-horizon radar, beamforming techniques are often used to suppress airspace radio frequency interference because the high-frequency band is shared by many devices. To address the problems that the traditional beamforming method is not capable of recognizing the electromagnetic environment and that its performance is greatly affected by the accuracy of signal feature estimation, a cognitive beamforming method using range-Doppler (RD) map features for skywave radar is proposed. First, the RD map is weighted by a local attention model, and then, texture features are extracted as the inputs to a support vector machine. Finally, the support vector machine is used to predict the optimal diagonal loading factor. Simulation results show that the output signal-to-interference-plus-noise ratio is improved compared with previous methods. The proposed method is suitable for many kinds of common unsatisfactory scenarios, making it beneficial for engineering implementation. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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21 pages, 3357 KiB  
Article
Moving Multitarget Detection Using a Multisite Radar System with Widely Separated Stations
by Shiyu Zhang, Yu Zhou, Minghui Sha, Linrang Zhang and Lan Du
Remote Sens. 2022, 14(11), 2660; https://doi.org/10.3390/rs14112660 - 2 Jun 2022
Cited by 4 | Viewed by 2103
Abstract
This study investigates the detection problem of multiple moving targets using a multisite radar system with widely separated stations. Spatial mapping is presented to integrate the observation data of a moving target from different angles into a spatial resolution cell (SRC). However, data [...] Read more.
This study investigates the detection problem of multiple moving targets using a multisite radar system with widely separated stations. Spatial mapping is presented to integrate the observation data of a moving target from different angles into a spatial resolution cell (SRC). However, data association errors occur in some SRCs in this way, which causes extra false alarm, called the “ghost target”. Therefore, an interference discriminator-based detector is developed. In this way, the background interference is discriminated between “ghost target” and pure noise, and then the final decision is made based on the generalized likelihood ratio test. Statistical analyses are provided to discuss the achievable performance. Simulation results show that the proposed algorithm can accurately detect multiple moving targets while suppressing the “ghost target”. Full article
(This article belongs to the Special Issue Small or Moving Target Detection with Advanced Radar System)
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