A Clutter Suppression Method Based on NSS-RPCA in Heterogeneous Environments for SAR-GMTI

Guo, Yifan; Liao, Guisheng; Li, Jun; Gu, Tong

doi:10.1109/tgrs.2020.2972060

Cited by 19 publications

(8 citation statements)

References 29 publications

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“…With the development of the low-rank sparse decomposition (LRSD) algorithm, robust principal component analysis (RPCA) has been used in SAR signals for various applications, such as clutter suppression and moving target detection by separating moving and stationary targets in SAR images [35][36][37][38][39]. In recent years, the theory of LRSD has been successfully applied to mitigate RFI in SAR data.…”

Section: Semi-parametric Methodsmentioning

confidence: 99%

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

Zhang

Lin

et al. 2022

Remote Sensing

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As an open system, synthetic aperture radar (SAR) inevitably receives radio frequency interference (RFI) generated by electromagnetic equipment in the same band. The existence of RFI seriously affects SAR signal processing and image interpretation. In recent years, many algorithms and models related to RFI mitigation have been proposed. However, most of that focus on effectively mitigating the RFI is insufficient to protect the useful signals. This article proposes a mitigation method of RFI with a signal-protected capability. (1) The kurtosis coefficient is used to detect RFI pulse-by-pulse, and the echoes containing RFI are stored in matrix form. (2) The preliminary extraction of RFI is complete by low-rank sparse decomposition of the echo matrix containing RFI. (3) For the secondary separation of RFI, the accurate position of RFI in the preliminary extraction results is located by the fuzzy C-means clustering; then, we separate the RFI and the remaining useful signals again and reconstruct the useful signals to complete the mitigation work. The proposed method can further protect useful signals while effectively removing interference through the secondary separation of RFI. Experimental results based on simulated and measured data verify the performance and potential of the proposed method.

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Section: Semi-parametric Methodsmentioning

confidence: 99%

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

Zhang

Lin

et al. 2022

Remote Sensing

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“…In Equation (18),

\mathbf{Y}

and

\mathbf{X}

denote the Lagrange multiplier,

{{\Vert}\bullet {\Vert}}_{\mathrm{F}}

is the Frobenius norm,

\mu

and

\zeta

are the penalty factor, and

\langle \mathbf{X},\mathbf{Y}\rangle =\hspace*{.5em}\text{trace}\left({\mathbf{X}}^{H}\mathbf{Y}\right)

represents the inner product of the two matrices. Optimization problem Equation (17) can be solved by using ADMM algorithm [29]. Then, Equation (17) can be transformed into five subproblems as follow

({, \begin{array}{l} {boldR}_{k + 1} : = \underset{boldR}{\arg min L (Y_{k}, X_{k}, boldR, J_{k}, T_{k}, α, μ, ζ)} \\ {boldT}_{k + 1} : = \underset{boldT}{\arg min L (Y_{k}, R_{k}, J_{k}, boldT, λ, μ)} \\ updata {boldC}_{boldJ}, {boldU}_{boldJ} and {boldR}_{boldJ} \\ {boldJ}_{k + 1} : = \arg min L ()...</p> \end{array})

…”

Section: Moving Target Detection Methods Based On Cur‐rpcamentioning

confidence: 99%

“…represents the inner product of the two matrices. Optimization problem Equation ( 17) can be solved by using ADMM algorithm [29]. Then, Equation ( 17) can be transformed into five subproblems as follow…”

Section: Solving the Proposed Optimization Problemmentioning

confidence: 99%

Moving target detection method based on CUR‐RPCA for missing array elements

Shi

Guo

Hua

et al. 2022

IET Radar Sonar & Navi

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Moving target detection performance is seriously affected by missing array elements for multichannel synthetic aperture radar (Multi-SAR) systems. Meanwhile, there is a contradiction between the accuracy of data recovery and the computational burden in traditional methods. To solve the problems, a moving target detection method based on CUR-RPCA for missing array elements is proposed in this paper. First, key nodes of the echo data are extracted and rearranged into a low-rank matrix. Then we uses the low-rank characteristic to complete key nodes of data. Subsequently, complete echo data is constructed by utilising recovered key nodes of data and CUR decomposition. Therefore using the ideas of CUR and RPCA, a novel optimization problem is constructed to achieve separation of moving targets and clutter while recovering data. In addition, only key nodes of the data is completed to achieve the performance of all data recovery, so that the disadvantage of low computational efficiency has been overcome for traditional matrix completion methods. Finally, the superior clutter suppression performance of the proposed method is verified by several numerical simulations and comparative studies.

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“…Airborne radar plays an important role in detecting aircraft, ships, and vehicles at long ranges [1]. However, due to the platform motion, slowly moving targets are easily masked by strong clutter; hence, clutter suppression is a challenging problem for airborne radar to overcome [2,3]. Traditional typical clutter suppression methods include displaced phase center antenna (DPCA) [4] and along-track interferometry (ATI) [5].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

et al. 2021

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Clutter suppression in heterogeneous environments is a serious challenge for airborne radar. To address this problem, a matrix-manifold-based clutter suppression method is proposed. First, the distributions of training data in heterogeneous environments are analyzed, while the received data are characterized on a Riemannian manifold of Hermitian positive definite matrices. It is indicated that the training data with different distributions with the same power are separated, whereas data with the same distribution are closer together. This implies that the underlying geometry of the data can be better revealed by manifolds than by Euclidean space. Based on these properties, homogeneous training data are selected by establishing a binary hypothesis test such that the negative effects of the use of heterogeneous samples are alleviated. Moreover, as exploiting a geometric metric on manifolds to reveal the underlying information of data, experimental results on both simulated and real data validate that the proposed method has a superior performance with small sample support.

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A Clutter Suppression Method Based on NSS-RPCA in Heterogeneous Environments for SAR-GMTI

Cited by 19 publications

References 29 publications

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

Moving target detection method based on CUR‐RPCA for missing array elements

Heterogeneous Clutter Suppression for Airborne Radar STAP Based on Matrix Manifolds

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