Robust Time-of-Arrival-Based Splitting Mean Moving Object Localization

Abstract

This letter presents a novel approach for accurately localizing moving object based on a robust time-of-arrival-based splitting mean positioning algorithm. The estimation performance of the existing localization method using the variational Bayesian Gaussian mixture model is degraded when a single observation is used. To overcome this limitation, the splitting mean online expectation maximization and closed-form solution are developed in this letter. The fundamental concept behind the proposed method involves splitting the mean of the approximate likelihood function into the true distance and bias components. These two components are estimated separately, enhancing the accuracy of localization. The simulation results demonstrate that the proposed algorithms outperform existing state-of-the-art methods in terms of localization performance.