Bayesian multipath-enhanced device-free localization: Simulation- and measurement-based evaluation

Abstract

Device-free localisation (DFL) systems infer presence and location of moving users by measuring to which extent they change the received signal power in wireless links. Consequently, 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 (MPCs). Since the propagation paths of MPCs 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 localisation (MDFL) system. Based on empirical models that relate perturbations in the received power of MPCs to the user location, the localisation problem can be solved by non-linear Bayesian filtering. In this work, we investigate the point mass filter and the particle filter as possible solutions. We demonstrate the applicability of these solutions using ultra-wideband measurements and develop and verify a numerical simulation framework that flexibly enables a sound evaluation of MDFL. Based on both measurements and simulations, we show a significant improvement of the localisation performance of MDFL compared to DFL. The overall localisation performance is thereby comparable for both filters. Eventually, we show that complexity and divergence probability, rather than localisation performance, are the decisive factors for the choice of the filter solution.