Rao-Blackwellized Gaussian Sum Particle Filtering for Multipath Assisted Positioning

Kurzfassung

In multipath-assisted positioning, every multipath component impinging at a receiver is regarded as being transmitted by a virtual transmitter in a pure line-of-sight condition. As the locations and clock offsets of the virtual and physical transmitters are in general unknown, simultaneous localization and mapping (SLAM) schemes can be applied to simultaneously localize a user and estimate the states of physical and virtual transmitters as landmarks. Hence, multipath-assisted positioning enables localizing a user with only one physical transmitter depending on the scenario. In this paper, we present and derive a novel filtering approach for our multipath-assisted positioning algorithm called Channel-SLAM. Making use of Rao-Blackwellization, the location of a user is tracked by a particle filter, and each landmark is represented by a sum of Gaussian probability density functions, whose parameters are estimated by unscented Kalman filters. Since data association, i.e., finding correspondences among landmarks, is essential for robust long-term SLAM, we also derive a data association method. We evaluate our filtering approach for multipath-assisted positioning by simulations in an urban scenario and by outdoor measurements.