Outlier Rejection for 5G-Based Indoor Positioning in Ray-Tracing-Enabled Industrial Scenario

TL;DR

This paper presents an IRLS-based iterative positioning method for 5G indoor localization in industrial environments, effectively rejecting outliers caused by NLoS conditions in multipath-rich scenarios.

Contribution

It introduces a robust outlier rejection scheme for 5G-based indoor positioning using IRLS, validated in ray-tracing industrial scenarios at multiple frequency bands.

Findings

IRLS effectively rejects outliers in ToA measurements.

The scheme performs well at both C-band and mmWave frequencies.

Robust positioning achieved in complex industrial multipath environments.

Abstract

The precise and accurate indoor positioning using cellular communication technology remains to be a prerequisite for several industrial applications, including the emergence of a new topic of Integrated Sensing and Communication (ISAC). However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in a heavy multipath dominant industrial scenario challenge the wireless signal propagation, leading to abnormal estimation errors (outliers) in the signal measurements taken at the receiver. In this paper, we investigate the iterative positioning scheme that is robust to the outliers in the Time of Arrival (ToA) measurements. The Iteratively Reweighted Least Squares (IRLS) positioning scheme formulated on the Least Squares (LS) is implemented to reject the outlier measurements and reweight the available ToA samples based on their confidence. Our positioning scheme is validated under 5G frequency bands, including the C-band (3.7 GHz) and the mmWave-band (26.8 GHz) in a Ray-Tracing enabled industrial scenario with different emulation setups.