Robust Elliptic Positioning via Sparse Regularization and ADMM for a Distributed MIMO Radar in the Presence of Outliers

This is the authors' abstract. We don't add key points for this paper.

Large-magnitude errors, known as outliers, have a significant impact on the accuracy of elliptic positioning (EP) for a distributed MIMO radar. Most of the existing EP techniques are developed in the least squares sense, susceptible to outliers. In this letter, we propose to leverage a sparse argument to model the outliers and decompose the measurement noise into two components, namely, inliers and outliers. By doing so, the robust EP is formulated as a₀-norm minimization problem with non-convex constraints. By relaxing the objective₀-norm with the convex₁-norm, an alternating direction method of multipliers based algorithm is derived to efficiently solve the relaxed optimization problem. Numerical results illustrate the improved performance of the proposed method compared to the existing methods.