Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge

TL;DR

This paper introduces a deep learning-based approach that uses Channel Impulse Response signals to mitigate ranging errors in ultra-wideband localization, especially in challenging NLoS indoor environments, enabling robust and low-power positioning.

Contribution

It presents a novel deep learning and graph optimization methodology that directly leverages CIR signals for effective UWB ranging error correction at the edge.

Findings

Effective error mitigation in NLoS conditions

Robust performance with low computational power

Validated through extensive experiments

Abstract

Ultra-wideband (UWB) is the state-of-the-art and most popular technology for wireless localization. Nevertheless, precise ranging and localization in non-line-of-sight (NLoS) conditions is still an open research topic. Indeed, multipath effects, reflections, refractions, and complexity of the indoor radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation at the edge. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions. Extensive experimentation with different settings and configurations has proved the effectiveness of our methodology and demonstrated the feasibility of a robust and low computational power UWB range error mitigation.