Unscented Kalman Filtering Based Multipath-assisted Positioning with Peak Flow Tracking

Abstract

Multipath-assisted positioning is a promising way to realize robust and accurate indoor positioning, taking advantage of the environmental information carried by multipath signals. In this paper, we propose a novel multipath-assisted time-of-arrival (TOA) positioning method for complex indoor scenarios without a-priori knowledge of the floor plan. We use virtual anchors (VAs) to model the propagation path of reflected signals. The trajectory of the user equipment (UE) and the locations of VAs are iteratively estimated using two unscented Kalman filters (UKFs), considering the changeable visibility of VAs due to the birth and death of multipath components (MPCs). To use TOA measurements of MPCs as input for the update phase of the UKF, we present a data association method based on multipath peak flow tracking by baseband signal processing to establish the correspondence between measurements and VAs. We derive the analytical solution using the received power of MPCs for multipath tracking, which can realize low computational complexity data association. Simulation results show that, most of the MPCs can be correctly tracked using the peak flow method, even if some MPCs are densely distributed. For the proposed iterative UKF, the mean square error of the UE's position, with associated measurements obtained by the peak flow method as input, is generally less than 0.42 m in the complex indoor scenario.