Multipath-Assisted Indoor Localization: Turning Multipath Signal from Enemy to Friend

In indoor environment, multipath signals are rich and contain indoor geometry information, which can be used to locate targets. Based on this, a multipath-assisted indoor localization algorithm is proposed, which is different from the conventional localization algorithm regarding multipath signal as enemy. Firstly, differential Time of Flight (ToF) of multipath signals are used to construct the fitness function of Particle Swarm Optimization (PSO) with respect to the locations of target and scatterers. Then, PSO is used to jointly estimate the locations of target and scatterers, in which the AoAs of target and scatterers are used to determine location ranges. Secondly, to improve localization accuracy further, we propose a novel detection algorithm of bad scatterers based on the mutually exclusive characteristic manifested by the correct and wrong locations of scatterers. Then, Affine Propagation Clustering (APC) is used for all target locations estimated by scatterers to determine if the result of PSO is acceptable with the proposed criterion.