MIMO Multipath-based SLAM for Non-Ideal Reflective Surfaces

TL;DR

This paper presents a Bayesian particle-based SPA method for MIMO MP-SLAM that effectively models and estimates non-ideal reflective surfaces causing dispersion effects, improving localization accuracy.

Contribution

It introduces a novel joint estimation approach for dispersion parameters of non-ideal surfaces in MIMO MP-SLAM using multiple-measurement data association.

Findings

Robust estimation of surface positions and dispersion extents

Effective handling of non-ideal reflective surfaces in simulations

Improved localization accuracy in complex environments

Abstract

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.