  Multipath-based simultaneous localization and mapping (MP-SLAM) is a well
established approach to obtain position information of transmitters and
receivers as well as information regarding the propagation environments in
future multiple input multiple output (MIMO) communication systems.
Conventional methods for MP-SLAM consider specular reflections of the radio
signals occurring at smooth, flat surfaces, which are modeled by virtual
anchors (VAs) that are mirror images of the physical anchors (PAs), with each
VA generating a single multipath component (MPC). However, non-ideal reflective
surfaces (such as walls covered by shelves or cupboards) cause dispersion
effects that violate the VA model and lead to multiple MPCs that are associated
to a single VA. In this paper, we introduce a Bayesian particle-based
sum-product algorithm (SPA) for MP-SLAM in MIMO communications systems. Our
method considers non-ideal reflective surfaces by jointly estimating the
parameters of individual dispersion models for each detected surface in delay
and angle domain leveraging multiple-measurement-to-feature data association.
We demonstrate that the proposed SLAM method can robustly and jointly estimate
the positions and dispersion extents of ideal and non-ideal reflective surfaces
using numerical simulation.
