Outlier-Robust Multistatic Target Localization

Abstract

Multistatic localization techniques employ noisy range measurements collected via multiple transmitters and receivers to localize a target. However, in many realistic scenarios the data are corrupted by outliers which may be due to the failure of or malicious attack on one or more sensors. The presence of outliers leads to performance degradation in terms of target localization accuracy. In this letter, we address the problem of multistatic target localization when the measurements contain outliers. We employ a multi-hypothesis testing method based on the false discovery rate (FDR) to detect the outliers. More specifically, we consider a penalized maximum likelihood problem for joint estimation of the number and positions of the outliers as well as the target position, and the noise variance. To solve this problem, an iterative algorithm employing the majorization-minimization technique that minimizes the objective in a monotonic manner is developed. Through numerical simulations, we compare the proposed algorithm with other robust state-of-the-art algorithms and show that the proposed algorithm has superior performance.