Performance Boosting for Two-Step Localization Methods in Distributed MIMO Radar via a Robust Post-Processing Scheme

In radar target localization using time delay (TD) measurements, reliable TD extraction is an important prerequisite for conventional two-step localization methods. In the distributed multiple-input multiple-output (MIMO) radar, the low-quality TD measurements (i.e., outliers) always occur due to the target scattering fluctuations in various transmitter-receiver paths, which results in serious performance degradation to the generic two-step localization methods. To this end, we propose a simple yet effective performance boosting scheme in this letter. It constructs a robust estimator based on the Huber loss function and solves this problem iteratively by using the majorization-minimization (MM) technique. This proposal is a general post-processing module, and thus it can be combined with any existing two-step algorithms to achieve performance improvement in the presence of outliers. The simulations verify the effectiveness of the proposed scheme by comparing with the derived theoretical lower bound. In addition, the parameter robustness and the computational burden are also evaluated.