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Robust Target Localization in Distributed MIMO Radar With Nonconvex ℓp Minimization and Iterative Reweighting | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

This letter deals with the problem of robust target localization in distributed multiple-input multiple-output (MIMO) radar using bistatic range measurements contaminated...Show More

Abstract:

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 \ell _{p} -norm for outlier rejection, in this letter, we reformulate the target localization problem as a nonconvex \ell _{p} -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 ( \ell _{p} -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.
Published in: IEEE Communications Letters ( Volume: 27, Issue: 12, December 2023)
Page(s): 3230 - 3234
Date of Publication: 10 October 2023

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