Cooperative mmWave PHD-SLAM with Moving Scatterers

TL;DR

This paper introduces a cooperative mmWave PHD-SLAM method that effectively tracks moving scatterers like vehicles, improving localization accuracy in vehicular scenarios with dynamic environments.

Contribution

It proposes two novel countermeasures for tracking moving vehicle scatterers within mmWave radio MM-PHD-SLAM, enhancing robustness against object movements.

Findings

Outperforms previous filters in vehicle scatterer scenarios

Improves vehicle localization accuracy in dynamic environments

Demonstrates effectiveness through simulation results

Abstract

Using the multiple-model (MM) probability hypothesis density (PHD) filter, millimeter wave (mmWave) radio simultaneous localization and mapping (SLAM) in vehicular scenarios is susceptible to movements of objects, in particular vehicles driving in parallel with the ego vehicle. We propose and evaluate two countermeasures to track vehicle scatterers (VSs) in mmWave radio MM-PHD-SLAM. First, locally at each vehicle, we generate and treat the VS map PHD in the context of Bayesian recursion, and modify vehicle state correction with the VS map PHD. Second, in the global map fusion process at the base station, we average the VS map PHD and upload it with self-vehicle posterior density, compute fusion weights, and prune the target with low Gaussian weight in the context of arithmetic average-based map fusion. From simulation results, the proposed cooperative mmWave radio MM-PHD-SLAM filter is shown to outperform the previous filter in VS scenarios.