Graph-based Simultaneous Localization and Bias Tracking

TL;DR

This paper introduces a graph-based, particle filter algorithm for accurate mobile localization in multipath environments, leveraging delay biases to estimate position without explicit environment mapping.

Contribution

It presents a novel factor graph and particle-based sum-product algorithm that jointly estimates position and multipath components using delay biases, improving robustness and accuracy.

Findings

Outperforms state-of-the-art multipath tracking algorithms.

Achieves performance close to the posterior Cramer-Rao lower bound.

Effectively identifies unreliable measurements to mitigate track loss.

Abstract

We present a factor graph formulation and particle-based sum-product algorithm for robust localization and tracking in multipath-prone environments. The proposed sequential algorithm jointly estimates the mobile agent's position together with a time-varying number of multipath components (MPCs). The MPCs are represented by "delay biases" corresponding to the offset between line-of-sight (LOS) component delay and the respective delays of all detectable MPCs. The delay biases of the MPCs capture the geometric features of the propagation environment with respect to the mobile agent. Therefore, they can provide position-related information contained in the MPCs without explicitly building a map of the environment. We demonstrate that the position-related information enables the algorithm to provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations. Using simulated and real measurements in different scenarios we demonstrate that the proposed algorithm significantly outperforms state-of-the-art multipath-aided tracking algorithms and show that the performance of our algorithm constantly attains the posterior Cramer-Rao lower bound (P-CRLB). Furthermore, we demonstrate the implicit capability of the proposed method to identify unreliable measurements and, thus, to mitigate lost tracks.