Outlier-Resistant Fusion for Multi-static Positioning using 5G NR Signals

TL;DR

This paper introduces an outlier-resistant fusion algorithm for multi-static indoor positioning using 5G NR signals, significantly improving robustness and accuracy in multipath-rich environments compared to existing methods.

Contribution

The paper presents a novel two-step algorithm that combines initial AoA estimation with Cauchy loss-based refinement to mitigate outliers in multipath environments.

Findings

Achieves less than 70 cm positioning error in 90% of cases.

Outperforms benchmark methods like IRLS, LS, and Huber loss.

Enhances tracking performance in cluttered indoor scenarios.

Abstract

Indoor positioning faces ongoing challenges due to complex propagation conditions, such as multipath propagation, signal blockages, and intrinsic target characteristics that substantially impact measurement reliability and positioning accuracy. Existing methods, in particular Least Squares (LS), frequently struggle to maintain robustness when confronted with unreliable observations caused by multipath interactions and extended targets. In this work, we propose an outlier-resistant algorithm designed to mitigate the impact of outlier measurements and accurately estimate the position of an extended target in multipath-rich environments. We develop a two-step algorithm in which an initial coarse position estimate is obtained using the angle-of-arrival (AoA) and subsequently refined using the Cauchy loss function to suppress outliers. The numerical results confirm that the proposed algorithm improves robustness and accuracy, outperforming existing benchmark methods, such as Iterative Reweighted Least Squares (IRLS), LS, and Huber loss function, and achieving a positioning error of less than $70$ cm in $90\%$ of cases. Its effectiveness in mitigating multipath effects is further assessed by comparing tracking performance in cluttered and empty room scenarios.