Localization of mobile nodes based on inaccurate round-trip-time measurements using Bayesian inference

Abstract

In this paper, we present a Bayesian-based localization method to estimate the unknown location of a mobile station (MS) in a network given the known location of a set of base stations. In this study, the MS was covered by a base station if the signal to interference and noise ratio (SINR) was larger than a threshold. By assuming a priori knowledge of the location of the MS and a Gaussian distribution for the round-trip time (RTT) measurements, a posteriori estimation of the unknown location of the MS using Bayesian inference method was obtained. The impact of variation of various variables on the localization accuracy such as number of base stations covering the MS, number of base stations interfering with the communication between the MS and the covering base stations, fading factor between base stations and the MS, SINR threshold and the path-loss coefficient have been studied. In addition, the performance of the developed Bayesian-based method in terms of localization accuracy was evaluated when the MS was inside and outside the convex hull of the base stations. The results of this study showed an improvement in the mean localization error and the localization root mean square error (RMSE) compared to other similar studies.