Multipath-based SLAM for Non-Ideal Reflective Surfaces Exploiting Multiple-Measurement Data Association

TL;DR

This paper presents a Bayesian particle-based SLAM method that effectively handles multiple multipath components associated with single virtual anchors, improving robustness in non-ideal reflective environments.

Contribution

It introduces a novel statistical measurement model and a particle-based algorithm to manage multiple measurements per virtual anchor in multipath SLAM.

Findings

Robustly fuses multiple measurements per VA in simulations

Handles measurement imperfections like non-calibrated antennas

Enhances multipath SLAM accuracy in rough surface environments

Abstract

Multipath-based simultaneous localization and mapping (SLAM) is a promising approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future mobile communication systems. Usually, specular reflections of the radio signals occurring at flat surfaces are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs). In existing methods for multipath-based SLAM, each VA is assumed to generate only a single measurement. However, due to imperfections of the measurement equipment such as non-calibrated antennas or model mismatch due to roughness of the reflective surfaces, there are potentially multiple multipath components (MPCs) that are associated to one single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for multipath-based SLAM that can cope with multiple-measurements being associated to a single VA. Furthermore, we introduce a novel statistical measurement model that is strongly related to the radio signal. It introduces additional dispersion parameters into the likelihood function to capture additional MPCs-related measurements. We demonstrate that the proposed SLAM method can robustly fuse multiple measurements per VA based on numerical simulations.