Angle Estimation for Bistatic MIMO Radar under Element Failure via Tensor Completion with Factor Priors - Chen, Jinli; Jiang, Zhijun; Zhu, Xicheng; Li, Jiaqiang (IEEE transactions on vehicular technology, 2023)
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.