Uncertainty-aware Gaussian Mixture Model for UWB Time Difference of Arrival Localization in Cluttered Environments

TL;DR

This paper introduces an uncertainty-aware Gaussian mixture model for UWB TDOA localization in cluttered environments, improving accuracy by modeling non-Gaussian noise and incorporating uncertainty into the noise estimation process.

Contribution

It proposes a novel bi-level optimization algorithm that jointly learns localization and noise models, explicitly incorporating uncertainty to enhance performance in complex environments.

Findings

Accurate localization achieved in cluttered environments.

Effective noise modeling with Gaussian mixture models.

Robust performance without prior obstacle knowledge.

Abstract

Ultra-wideband (UWB) time difference of arrival(TDOA)-based localization has emerged as a low-cost and scalable indoor positioning solution. However, in cluttered environments, the performance of UWB TDOA-based localization deteriorates due to the biased and non-Gaussian noise distributions induced by obstacles. In this work, we present a bi-level optimization-based joint localization and noise model learning algorithm to address this problem. In particular, we use a Gaussian mixture model (GMM) to approximate the measurement noise distribution. We explicitly incorporate the estimated state's uncertainty into the GMM noise model learning, referred to as uncertainty-aware GMM, to improve both noise modeling and localization performance. We first evaluate the GMM noise model learning and localization performance in numerous simulation scenarios. We then demonstrate the effectiveness of our algorithm in extensive real-world experiments using two different cluttered environments. We show that our algorithm provides accurate position estimates with low-cost UWB sensors, no prior knowledge about the obstacles in the space, and a significant amount of UWB radios occluded.