Robust Target Localization in Distributed MIMO Radar with Nonconvex ℓp Minimization and Iterative Reweighting

Publication Name

IEEE Communications Letters


This letter deals with the problem of robust target localization in distributed multiple-input multiple-output (MIMO) radar using bistatic range measurements contaminated by outliers. Motivated by the robustness of nonconvex ℓp-norm for outlier rejection, in this letter, we reformulate the target localization problem as a nonconvex ℓp-norm minimization of residual matrix with nonconvex quadratic constraints. However, the resulted problem is very challenging. We consider the use of iterative reweighting algorithms, which approximates the nonconvex problem by a sequence of tractable subproblems. In particular, a new weight update method is proposed to accommodate the solving algorithm of the subproblem and avoid the selection of a regularization parameter, leading to an improved iterative reweighting (ℓp-IIRW) solution. Numerical results demonstrate substantially enhanced robustness and improved positioning accuracy of the proposed method in both cases of low signal-to-interference-plus-noise ratio (SINR) outliers and non-line-of-sight (NLOS) outliers.

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