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