Multipath Assisted Positioning with Simultaneous Localization and Mapping

Abstract

This paper describes an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply a novel algorithm based on recursive Bayesian filtering, named Channel-SLAM. This approach treats multipath components as signals emitted from virtual transmitters which are time synchronized to the physical transmitter and static in their positions. Contrarily to other approaches, Channel-SLAM considers also paths occurring due to multiple numbers of reflections or scattering as well as the combination. Hence, each received multipath component increases the number of transmitters resulting in a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of the virtual transmitters simultaneously, hence, the approach does not require any prior information such as a room-layout or a database for fingerprinting. The only prior knowledge needed is the physical transmitter position as well as the initial receiver position and moving direction. Based on simulations, the position precision of Channel-SLAM is evaluated by a comparison to simplified algorithms and to the posterior Cramér-Rao lower bound. Furthermore, the paper shows the performance of Channel-SLAM based on measurements in an indoor scenario with only a single physical transmitter.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…A site-specific propagation model is used for indoor localization in 5G to exploit multipath in mmWave MIMO channels [45]. Along the same line, Channel-SLAM is a recursive Bayesian filtering approach that treats multipath components as signals emitted from virtual transmitters, thus leading to a more accurate position estimate or enabling positioning when the number of physical transmitters is insufficient [49].…”

Log-PF: Particle Filtering in Logarithm Domain

“…In this section we consider a simultaneous localization and mapping (SLAM) example with radio signals indicated in Figure 5 according to the system model in [19] (similar results are expected in, e.g., belief propagation [20] or distributed PFs [13]). A receiver which is moving along an arbitrary trajectory is measuring the distances…”

“…However, for notational convenience, a time dependence on is introduced here for the transmitter positions r , , . Additionally, we assume based on [19] that the distance offset , is constant.…”

“…The state space is estimated by an algorithm according to [19,21] based on Rao-Blackwellization [22], where the states space of x is partitioned into subspaces. Hence, we use PFs to estimate the subspaces representing the transmitters inside a PF.…”

“…The reason to use a PF instead of a low complexity extended Kalman filter (EKF) is the nonlinearity of the measurements in (29). The algorithm of [19,21] is based on a superordinate particle filter (superPF) and subordinate particle filters (subPFs). Each particle = 1, .…”