Iterated Posterior Linearization PMB Filter for 5G SLAM

TL;DR

This paper introduces an iterated posterior linearization filter integrated into a Poisson multi-Bernoulli SLAM framework for 5G mmWave signals, improving localization and mapping accuracy by better handling measurement nonlinearity.

Contribution

It proposes a novel linearization approach with respect to the posterior PDF and incorporates it into SLAM, enhancing performance over traditional methods.

Findings

Improved accuracy and precision in 5G SLAM simulations.

Effective handling of measurement nonlinearity.

Enhanced localization and mapping results.

Abstract

5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the accuracy and precision improvements of the resulting SLAM filter.