Quantcast

MLE-based localization and performance analysis in probabilistic LOS/NLOS environment

Research paper by XiufangShia, GuoqiangMaobcd, ZaiyueYanga, JimingChena

Indexed on: 03 Nov '17Published on: 01 Dec '17Published in: Neurocomputing



Abstract

Non-line-of-sight (NLOS) propagation, which widely exists in wireless systems, will degrade the performance of wireless positioning system if it is not taken into consideration in the localization algorithm design. Different from existing approaches which treat NLOS measurements as outliers and only work when there is a large number of measurements, in this paper, we propose to use Maximum Likelihood Estimator (MLE) for localization, which utilizes all the available measurements and explicitly takes the probabilities of occurrences of LOS and NLOS propagations into account. Furthermore, to evaluate the accuracy of the proposed localization algorithm, the position error bound of the positioning system is derived using Cramer–Rao Lower Bound (CRLB). Through numerical analysis, the impact of NLOS propagation on the position error bound is evaluated. The performance of our proposed algorithm is verified by both simulations and real world experimental data.