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ADMM-Based Low-Complexity Off-Grid space-time adaptive processing Methods

IEEE ACCESS 2020年 8卷 206646-206658页

作者： Li, Zhongyue Wang, Tong Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

In this paper, we consider the problems of off-grid effects elimination and fast implementations for sparse recovery based space-time adaptive processing (SR-STAP) methods. To improve the computational efficiency of recently proposed atomic norm minimization based space-time adaptive processing (ANM-STAP) method, we derive a fast iterative scheme by exploiting the framework of the alternating direction method of multipliers (ADMM), where the unknown parameters are iteratively updated with closed-form expressions. Furthermore, to bypass the selection of regularization parameter in ANM-STAP, we also develop two novel gridless STAP methods by utilizing the covariance fitting criterion (CFC) and the properties of the clutter plus noise matrix (CNCM). Likewise, the corresponding ADMM-based fast implementations are also derived for both CFC-based methods to reduce their computational complexities. Simulation results with both simulated and Mountain-Top data demonstrate that high computational efficiency and good performance of proposed algorithms are achieved.

关键词： Clutter Training Covariance matrices Manganese Matching pursuit algorithms Iterative methods Estimation Airborne radar clutter suppression off-grid effect space-time adaptive processing sparse recovery

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Off-Grid Angle-Doppler Estimation for space-time adaptive processing: A Sequential Approach

Off-Grid Angle-Doppler Estimation for Space-Time Adaptive Pr...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Lu, Lei Zhou, Chengwei Shi, Zhiguo Chen, Jiming Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728107325

The accuracy and efficiency of angle-Doppler estimation are critical for space-time adaptive processing (STAP). These two demands, however, are usually contradictory. In this paper, we propose a sequential approach for off-grid angle-Doppler estimation in the framework of STAP, where both the accuracy and efficiency are well balanced. The sequential characteristic of the proposed algorithm can be reflected from two aspects. On one hand, the angle-Doppler estimation is accomplished via a two-stage procedure, where the coarse sparse reconstruction and the rotational invariant technique are sequentially incorporated. On the other hand. the closed-form solutions to off-grid angle and Doppler parameters are sequentially formulated, which are subsequently paired via an Euclidean distance minimization-based principle. Benefiting from such sequential implementation, the computational complexity can be greatly reduced, and the basis mismatch problem can be avoided in the meantime. Simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： Angle-Doppler estimation complexity off-grid sequential space-time adaptive processing

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Sparsity-based space-time adaptive processing using convolutional neural network

Sparsity-based space-time adaptive processing using convolut...

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IET International Radar Conference (IET IRC 2020)

作者： K. Duan X. Yang H. Xie Y. Wang Sun Yat-sen University (SYSU) School of Electronics and Communication Engineering 510006 Guangzhou China Wuhan Early Warning Academy 430019 Wuhan China

In this paper, a deep learning framework for space-time adaptive processing is developed. Firstly, a set of clutter covariance matrixes (CCMs) are modeled based on the prior parameters of radar and navigation system with respect to all possible levels of non-ideal factors, and the columns of each CCM is formulated as undersampled noisy linear measurements of the sparse coefficients corresponding to angle-Doppler spectrum. Then the original spectrum coefficients, obtained by least-square estimation from the modeled CCMs and known space-time steering dictionary, are used as input to train the convolutional neural network (CNN). Meanwhile, the corresponding labels can be obtained by the exact spectrum of modeled CCMs via minimum variance distortionless response algorithm. Once trained, the CNN can be used to predict angle-Doppler spectrum coefficients that corresponds to a new measurement vector in near real time. Simulations results have demonstrated the superiority of the proposed method in both clutter suppression performance and computation efficiency.

关键词： radar clutter space-time adaptive processing covariance matrices radar signal processing deep learning (artificial intelligence) convolutional neural nets Doppler radar interference suppression least squares approximations radar computing radionavigation

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Exploiting the Persymmetric Property of Covariance Matrices for Knowledge-Aided space-time adaptive processing

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IEEE ACCESS 2018年 6卷 68001-68012页

作者： Zhao, Yu Wan, Shaohua Lu, Songtao Sun, Jinping Lei, Peng Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Hubei Peoples R China Univ Minnesota Twin Cities Dept Elect & Comp Engn Minneapolis MN 55455 USA

In space-time adaptive processing (STAP) technique, the estimation of the interference-plusnoise covariance matrix is one of the critical points. Incorporating a priori knowledge into STAP architectures can reduce the effect of the heterogeneous environment and substantially improve the estimation accuracy of the covariance matrix. Besides the prior information, the persymmetric structure in radar systems with symmetric spaced linear array and constant pulse repetition interval can also be exploited to improve the STAP performance. In this paper, we present a new computationally adaptive knowledge-aided STAP method that requires fewer samples by utilizing the persymmetric structure of the covariance matrix. In addition, based on the covariance matrix estimation technology of the newly proposed knowledge-aided STAP method, two knowledge-aided persymmetric adaptive detectors in the nonhomogeneous environment are proposed as well. First, a two-step design procedure-based detector is proposed for the partially homogeneous model, which is called knowledge-aided persymmetric adaptive coherence estimator. Second, we improve the stochastic heterogeneous model and propose a new knowledge-aided persymmetric generalized likelihood ratio test for this model. Finally, simulation results confirm the effectiveness of the proposed methods.

关键词： space-time adaptive processing knowledge-aided persymmetric general linear combination convex combination adaptive signal detection

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Reduced-dimension sparse representation-based space-time adaptive processing method for airborne radar using simplified time-time transform spectrum

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷

作者： Wang, Qiang Zhang, Yongshun Guo, Yiduo Li, Zhihui Air Force Engn Univ Air & Missile Def Coll Xian Shaanxi Peoples R China

Considering the high computational burden of typical space-time adaptive processing (STAP) based on sparse representation (SR) (SR-STAP) method, a reduced-dimension (RD) SRSTAP method using simplified time-time (STT) transform spectrum is proposed to overcome this issue. First, the STT transform spectrum formula of clutter on cell under test (CUT) is deduced and the main energy of CUT in the STT transform domain is extracted. Second, to design the RD matrix, an adjustable RD threshold is defined, which is used to make a comparison with STT transform spectrum energy. Third, the RD SR dictionary is constructed to estimate the clutter spatial-temporal spectrum. Numerical simulation results demonstrate that the proposed sparse representation based on simplified time-time-STAP method reduces the computational burden significantly and has a highly similar clutter suppression performance compared with the typical SR-STAP. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： sparse representation time-time transform space-time adaptive processing reduced-dimension

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Modified Generalized Sidelobe Canceller for Nonuniform Linear Array Radar space-time adaptive processing

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2018年第9期E101A卷 1585-1587页

作者： Zhao, Xiang He, Zishu Wang, Yikai Jiang, Yuan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu Sichuan Peoples R China

This letter addresses the problem of space-time adaptive processing (STAP) for airborne nonuniform linear array (NLA) radar using a generalized sidelobe canceller (GSC). Due to the difficulty of determining the spatial nulls for the NLAs, it is a problem to obtain a valid blocking matrix (BM) of the GSC directly. In order to solve this problem and improve the STAP performance, a BM modification method based on the modified Gram-Schmidt orthogonalization algorithm is proposed. The modified GSC processor can achieve the optimal STAP performance and as well a faster convergence rate than the orthogonal subspace projection method. Numerical simulations validate the effectiveness of the proposed methods.

关键词： space-time adaptive processing generalized sidelobe canceller nonuniform linear array modified Gram-Schmidt orthogonalization

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Eigensubspace method for space-time adaptive processing in the presence of non-i.i.d. clutter and array errors

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IET RADAR SONAR AND NAVIGATION 2018年第7期12卷 757-765页

作者： Liu, Aifei Baker, Christopher J. Teh, Kah Chan Sun, Hongbo Gao, Caicai Harbin Engn Univ Acoust Sci & Technol Lab Harbin Heilongjiang Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin Heilongjiang Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Univ Birmingham Dept Elect Elect & Syst Engn Birmingham W Midlands England

This study examines space-time adaptive processing in the presence of non-independent and identically distributed (i.i.d.) clutter and array errors. The authors propose a clutter rank estimation method by exploring the spatial-temporal steering vectors of clutter. The proposed method is independent of clutter statistics and direction-independent array errors. They prove that when the proposed clutter rank estimation is used, the estimate of the clutter subspace is asymptotically independent of clutter statistics. This enables an eigensubspace method to acquire the asymptotic independence on clutter statistics. In addition, they prove that the eigensubspace method can suppress the clutter regardless of direction-independent array errors. They also suggest a geometrical non-homogeneity detector for the eigensubspace method. Simulation and experimental results with multi-channel airborne radar measurement (MCARM) data confirm that the eigensubspace method can suppress non-i.i.d. clutter such as discrete clutter as well as correlated clutter regardless of array gain-phase errors. The ability to suppress clutter regardless of clutter statistics and direction-independent array errors makes the eigensubspace method unique and feasible to the practical scenario when clutter is non-i.i.d. and the direction-independent array errors are present.

关键词： eigenvalues and eigenfunctions space-time adaptive processing estimation theory vectors statistical analysis radar clutter radar signal processing array signal processing eigensubspace method space-time adaptive processing noni i d clutter statistics nonindependent and identically distributed clutter clutter rank estimation method spatial-temporal steering vector direction-independent array error geometrical nonhomogeneity detector MCARM data array gain-phase error

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Sparse Bayesian Learning-Based space-time adaptive processing With Off-Grid Self-Calibration for Airborne Radar

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IEEE ACCESS 2018年 6卷 47296-47307页

作者： Yuan, Huadong Xu, Hong Duan, Keqing Xie, Wenchong Liu, Weijian Wang, Yongliang Wuhan Early Warning Acad Wuhan 430019 Hubei Peoples R China PLA Naval Univ Engn Wuhan 430033 Hubei Peoples R China

space-time adaptive processing (STAP) for airborne radar has recently been enriched owing to the development of methods based on sparse recovery techniques. These methods have shown advantages over the conventional ones. However, there are still difficulties in practical situations, for example, when many clutter components are not located on the discretized sampling grids of the dictionary, which will result in significant performance loss. To deal with such off-grid problem, this paper proposes a sparse Bayesian learning-based STAP (SBL-STAP) with an off-grid self-calibration method, which can effectively mitigate the off-grid effect. In the proposed method, the clutter plus noise covariance matrix is estimated via SBL. Meanwhile, we construct a small-scale complementary dictionary with an adaptive approach to calibrate the uniformly discretized dictionary. In each iteration of the SBL, the atoms of the complementary dictionary renew themselves by an approach based on weighted least squares. In this way, the atoms of complementary dictionary can converge to the clutter ridge adaptively when off-grid occurs. The simulation results show that the clutter ridge spreading caused by off-grid can be mitigated effectively, and the output signal-to-clutter-plus-noise ratio of the STAP is significantly improved. The benefits come at the cost of negligible increase of computational complexity.

关键词： Sparse Bayesian learning space-time adaptive processing off-grid self-calibration

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Reduced-dimension space-time adaptive processing for airborne radar with co-prime array

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JOURNAL OF ENGINEERING-JOE 2019年第19期2019卷 5971-5974页

作者： Wang, Xiaoye Yang, Zhaocheng Huang, Jianjun Shenzhen Univ Coll Informat Engn Shenzhen Peoples R China

space-time adaptive processing (STAP) for airborne radar with co-prime arrays is shown to have excellent superiority compared to traditional STAP with uniform linear array radar. However, high arithmetic computational complexity and large amount of training data are required in this approach. This motivates the authors to present a new approach which is relatively low computational load and fast convergence with satisfactory performance. Specifically, a reduced-dimension transformation in Doppler domain is incorporated into the existing approach. The reduced-dimension interference covariance matrix and target steering vector are then achieved by performing the reduced-dimension process, and hence two reduced-dimension STAP filters are designed using the derived interference covariance matrix and target-steering vector. Numerical simulations are carried out to reveal the superiority of the proposed approach.

关键词： space-time adaptive processing radar clutter computational complexity radar signal processing covariance matrices airborne radar derived interference covariance matrix reduced-dimension STAP filters reduced-dimension process target steering vector reduced-dimension interference covariance matrix existing approach reduced-dimension transformation satisfactory performance computational load training data high arithmetic computational complexity uniform linear array radar traditional STAP excellent superiority co-prime array airborne radar reduced-dimension space-time adaptive processing

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Dimension-reduced bi-iterative space-time adaptive processing method for airborne radar

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JOURNAL OF ENGINEERING-JOE 2019年第21期2019卷 8138-8141页

作者： Wang, Yuxiang Li, Xiaoming Gao, Wei AVIC Leihua Elect Technol Res Inst Wuxi Jiangsu Peoples R China

A novel bi-iterative dimension-reduced space-time adaptive processing (STAP) algorithm for clutter suppression and moving target detection in airborne radar system is proposed. A dimension-reduced processing in both space and time is firstly performed. Then a bi-quadratic cost function is constructed to estimate the dimension-reduced spatiotemporal filter coefficients, which are used for suppressing the clutter and keeping the target signal. An optimal solution to the cost function can be obtained efficiently by a bi-iteration algorithm. Theoretical analysis and computer simulation results illustrate that the algorithm has fast convergence, low computation load and sampling requirements. The results of experiment by using the measured data show the validity of the proposed algorithm.

关键词： airborne radar radar signal processing object detection radar clutter space-time adaptive processing iterative methods airborne radar system dimension-reduced processing bi-quadratic cost function dimension-reduced spatiotemporal filter coefficients bi-iteration algorithm dimension-reduced bi-iterative space-time adaptive processing method novel bi-iterative dimension-reduced space-time adaptive processing algorithm clutter suppression moving target detection

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