Angle Estimation for Bistatic MIMO Radar under Element Failure via Tensor Completion with Factor Priors

The presence of element failure results in an inevitable performance loss in angle estimation in multiple-input multiple-output (MIMO) radar. In this paper, we consider the angle estimation problem for bistatic MIMO radar under element failure. To exploit the multidimensional structure, a covariance...

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Vydané v:	IEEE transactions on vehicular technology Ročník 72; číslo 12; s. 1 - 14
Hlavní autori:	Chen, Jinli, Jiang, Zhijun, Zhu, Xicheng, Li, Jiaqiang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.12.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms angle estimation Antenna arrays array element failure Arrays CANDECOMP/PARAFAC decomposition Covariance matrices Estimation factor priors Failure Hankel matrices Linear arrays Mathematical analysis MIMO communication MIMO radar Multistatic radar Optimization Radar antennas Radar arrays tensor completion Tensors
ISSN:	0018-9545, 1939-9359
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Abstract	The presence of element failure results in an inevitable performance loss in angle estimation in multiple-input multiple-output (MIMO) radar. In this paper, we consider the angle estimation problem for bistatic MIMO radar under element failure. To exploit the multidimensional structure, a covariance tensor of the uniform linear array (ULA)-based MIMO radar is constructed, where some slices are entirely missing due to faults in the array elements. Then, recovering failed-element signals can be formulated as a low-rank tensor completion (LRTC) problem with structurally missing entries. To address this problem, we propose a novel tensor completion approach via CANDECOMP/PARAFAC decomposition with factor priors. The essence of the proposed method is to fully exploit not only the Vandermonde structure of factor matrices but also their correlations. To enforce these factor priors, we formulate an optimization problem that consists of an objective function penalizing the nuclear norm of block Hankel matrices formed by the factor matrices and the constraints to reveal the relationship among the factor matrices. To solve the optimization problem, we develop an algorithm based on the alternating direction method of multipliers (ADMM), thereby recovering the signals of failed elements. Finally, conventional algorithms yield robust angle estimation. Simulation results verify the effectiveness of the proposed algorithm for dealing with element failure.
AbstractList	The presence of element failure results in an inevitable performance loss in angle estimation in multiple-input multiple-output (MIMO) radar. In this article, we consider the angle estimation problem for bistatic MIMO radar under element failure. To exploit the multidimensional structure, a covariance tensor of the uniform linear array (ULA)-based MIMO radar is constructed, where some slices are entirely missing due to faults in the array elements. Then, recovering failed-element signals can be formulated as a low-rank tensor completion (LRTC) problem with structurally missing entries. To address this problem, we propose a novel tensor completion approach via CANDECOMP/PARAFAC decomposition with factor priors. The essence of the proposed method is to fully exploit not only the Vandermonde structure of factor matrices but also their correlations. To enforce these factor priors, we formulate an optimization problem that consists of an objective function penalizing the nuclear norm of block Hankel matrices formed by the factor matrices and the constraints to reveal the relationship among the factor matrices. To solve the optimization problem, we develop an algorithm based on the alternating direction method of multipliers (ADMM), thereby recovering the signals of failed elements. Finally, conventional algorithms yield robust angle estimation. Simulation results verify the effectiveness of the proposed algorithm for dealing with element failure. The presence of element failure results in an inevitable performance loss in angle estimation in multiple-input multiple-output (MIMO) radar. In this paper, we consider the angle estimation problem for bistatic MIMO radar under element failure. To exploit the multidimensional structure, a covariance tensor of the uniform linear array (ULA)-based MIMO radar is constructed, where some slices are entirely missing due to faults in the array elements. Then, recovering failed-element signals can be formulated as a low-rank tensor completion (LRTC) problem with structurally missing entries. To address this problem, we propose a novel tensor completion approach via CANDECOMP/PARAFAC decomposition with factor priors. The essence of the proposed method is to fully exploit not only the Vandermonde structure of factor matrices but also their correlations. To enforce these factor priors, we formulate an optimization problem that consists of an objective function penalizing the nuclear norm of block Hankel matrices formed by the factor matrices and the constraints to reveal the relationship among the factor matrices. To solve the optimization problem, we develop an algorithm based on the alternating direction method of multipliers (ADMM), thereby recovering the signals of failed elements. Finally, conventional algorithms yield robust angle estimation. Simulation results verify the effectiveness of the proposed algorithm for dealing with element failure.
Author	Chen, Jinli Jiang, Zhijun Li, Jiaqiang Zhu, Xicheng
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Snippet	The presence of element failure results in an inevitable performance loss in angle estimation in multiple-input multiple-output (MIMO) radar. In this paper, we... The presence of element failure results in an inevitable performance loss in angle estimation in multiple-input multiple-output (MIMO) radar. In this article,...
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SubjectTerms	Algorithms angle estimation Antenna arrays array element failure Arrays CANDECOMP/PARAFAC decomposition Covariance matrices Estimation factor priors Failure Hankel matrices Linear arrays Mathematical analysis MIMO communication MIMO radar Multistatic radar Optimization Radar antennas Radar arrays tensor completion Tensors
Title	Angle Estimation for Bistatic MIMO Radar under Element Failure via Tensor Completion with Factor Priors
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