Multipath Identification, User Localization, and Environment Mapping in Radio SLAM

Radio simultaneous localization and mapping (SLAM) is challenging due to multipath propagation. While line-of-sight (LoS) and first-order non-LoS (NLoS) paths, referred to as NLoS-1 paths, play a critical role in SLAM, no existing techniques can effectively separate them from high-order NLoS paths, i.e., NLoS-
n
paths (
n
≥ 2). This paper presents a new framework to accurately identify the LoS/NLoS-1 paths and conduct SLAM. The key idea is to define the virtual user equipment (UE) of a NLoS-
n
path as the
n
-th order reflection of the UE. We discover that the centers of the circles encompassing the UE, a virtual UE associated with a LoS/NLoS-1 path, and each of some other virtual UEs are aligned in a line, if and only if those virtual UEs are all associated with NLoS-1 paths. Accordingly, we propose to identify the LoS/NLoS-1 paths using Hough transform-based line detection, and estimate the UE’s location and the environments with the identified LoS/NLoS-1 paths using maximum likelihood estimation and mean-shift clustering. We analytically confirm that the localization error asymptotically approaches the Cramér-Rao Lower Bound. Simulations show that our approach outperforms the state of the art in localization accuracy by up to 91.93%, even when the latter assumed all NLoS-1 paths are perfectly identified
a-priori
.