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.

Robust ultra-wideband range error mitigation with deep learning at the edge / Angarano, Simone; Mazzia, Vittorio; Salvetti, Francesco; Fantin, Giovanni; Chiaberge, Marcello. - In: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. - ISSN 0952-1976. - ELETTRONICO. - 102:(2021), pp. 104278-104286. [10.1016/j.engappai.2021.104278]

Robust ultra-wideband range error mitigation with deep learning at the edge

Simone Angarano;Vittorio Mazzia;Francesco Salvetti;Giovanni Fantin;Marcello Chiaberge
2021

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.
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S0952197621001251-main.pdf

non disponibili

Descrizione: Post-print versione editoriale
Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.8 MB
Formato Adobe PDF
1.8 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
2011.14684.pdf

Open Access dal 05/05/2023

Descrizione: Post-print
Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: Creative commons
Dimensione 3.38 MB
Formato Adobe PDF
3.38 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2898272