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

• URL: http://arxiv.org/abs/2011.14684v2
• Date: Wed, 28 Apr 2021 09:16:00 GMT
• Title: Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge
• Authors: Simone Angarano, Vittorio Mazzia, Francesco Salvetti, Giovanni Fantin and Marcello Chiaberge
• Abstract summary: Multipath effects, reflections, refractions, and complexity of the indoor radio environment can introduce a positive bias in the ranging measurement. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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