Exploiting Diffuse Multipath in 5G SLAM

TL;DR

This paper introduces a novel method for 5G SLAM that leverages diffuse multipath signals, traditionally considered noise, to improve environment mapping in vehicular networks.

Contribution

The paper proposes a new approach that exploits diffuse multipath in 5G SLAM, integrating it into a probabilistic framework for enhanced mapping accuracy.

Findings

Simulation results show improved mapping performance.

Diffuse multipath signals can be effectively utilized.

The method outperforms traditional approaches that ignore diffuse multipath.

Abstract

5G millimeter wave (mmWave) signals can be used to jointly localize the receiver and map the propagation environment in vehicular networks, which is a typical simultaneous localization and mapping (SLAM) problem. Mapping the environment is challenging, due to measurements comprising both specular and diffuse multipath components, and diffuse multipath is usually considered as a perturbation. We here propose a novel method to utilize all available multipath signals from each landmark for mapping and incorporate this into a Poisson multi-Bernoulli mixture for the 5G SLAM problem. Simulation results demonstrate the efficacy of the proposed scheme.