Robust Moving Target Localization in Distributed MIMO Radars via Iterative Lagrange Programming Neural Network

Moving target localization based on the distributed multiple-input multiple-output (MIMO) radar has attracted great research interest recently. However, the occurrence of outliers in the measurements is always unavoidable in many practical situations, which degrades the performances of ordinary algorithms significantly. In this paper, a robust moving target localization method is proposed to tackle this issue. We first present the relevant maximum likelihood (ML) estimation, and recast it to a constrained optimization problem afterwards. We employ the Lagrange programming neural network (LPNN) framework to solve it due to its effectiveness for nonconvex optimization problems. Furthermore, to mitigate the adverse influence caused by outliers, an iterative reweighting scheme is developed and integrated with the LPNN. The target position and velocity estimations can be refined through an iteration process. Simulation results demonstrate that our proposed method not only has an outstanding performance under the Gaussian measurement noise, but is also very robust against outliers.