Robust TOA-Based Source Self-Positioning With Clock Imperfection

Pengcheng He, Xiaohu Jiang, Qingjiang Shi

Abstract: Source positioning is a key issue in a range of applications such as sensing, monitoring and tracking, etc. In this paper, we address the source localization problem with unknown clock skew and outliers in the TOA measurements. Considering the measurement outliers, we present a robust localization formulation by introducing Huber loss. Furthermore, we develop a lightweight iterative localization algorithm using majorization-minimization (MM) method with guaranteed convergence. In addition, we propose a novel semidefinite-relaxation-based algorithm for the proposed robust localization formulation. Simulations demonstrate that our proposed MM-based localization algorithms could achieve better performance than SDR-based localization algorithms in terms of both localization accuracy and computational time, for both cases with or without outliers.