Bayesian Approaches to Multipath-Enhanced Device-Free Localization

Abstract

Device-free localization systems infer presence and location of moving users by measuring user-induced perturbations in the signal power between wireless network nodes. Thereby, users not only induce perturbations to the power of the line-of-sight, but also to the power of reflected and scattered signals which are observed in the received signal as multipath components. Since the propagation paths of multipath components differ inherently from the line-of-sight path, these propagation paths can be considered as additional network links. This extended network determines the multipath-enhanced device-free localization system. Based on empirical models that relate perturbations in the power of multipath components to the user location, the localization problem can be solved by nonlinear Bayesian filtering. In this work, we therefore investigate the point mass filter and the particle filter as possible solutions. Using simulations, we demonstrate the applicability of these filter solutions for multipath-enhanced device-free localization. The overall localization performance is comparable for both filters. Further, the simulation results indicate an improvement of the localization performance of multipath-enhanced device-free localization compared to device-free localization.