A Wi-Fi-based indoor positioning algorithm with mitigating the influence of NLOS

Abstract

Indoor positioning has been getting more attention of researchers for academic as well as industrial point of view. However, because the propagation of Wi-Fi signals is greatly affected by the serious NLOS (non-line-of sight) effect for indoor environment. Therefore, the accuracy measure is deteriorated due to random NLOS effect in indoor wireless communication. In this paper, we propose a localization algorithm which is different from existing algorithms because no priori knowledge is required about NLOS conditions when working with the proposed algorithm. It utilizes both TOA (time-of-arrival) and received-signal-strength (RSS) measurements to get an optimized range of data based on the difference of two geometric area. And, then substitute this optimized data into the triangle centroid algorithm to perform positioning operation. Simulation results show that the proposed algorithm can efficiently reduce the error due to NLOS effect and achieve higher accuracy of localization as compared to other conventional algorithms.