Robust ultra-wideband range error mitigation with deep learning at the edge. (June 2021)

• Record Type: Journal Article
• Authors: Angarano, Simone; Mazzia, Vittorio; Salvetti, Francesco; Fantin, Giovanni; Chiaberge, Marcello
• Is Part Of: Engineering applications of artificial intelligence. Volume 102(2021)
• Publication Date: 2021-06
• Subjects: Deep learning -- Ultra-wideband -- Edge AI -- Indoor positioning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104278
• Languages: English
• ISSNs: 0952-1976

Abstract

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.