NLOS Identification and Mitigation Using Low-Cost UWB Devices

被引:91
作者
Barral, Valentin [1 ]
Escudero, Carlos J. [1 ]
Garcia-Naya, Jose A. [1 ]
Maneiro-Catoira, Roberto [1 ]
机构
[1] Univ A Coruna, Univ Coruna, CITIC Res Ctr, Campus Elvina, A Crouna 15071, Spain
关键词
UWB; machine learning; NLOS identification; GAUSSIAN-PROCESSES; LOCALIZATION;
D O I
10.3390/s19163464
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate distance measurements. Although promising, UWB devices also suffer from the classic problems found when working in indoor scenarios, especially when there is no a clear line-of-sight (LOS) between the emitter and the receiver, causing the estimation error to increase up to several meters. In this work, machine learning (ML) techniques are employed to analyze several sets of real UWB measurements, captured in different scenarios, to try to identify the measurements facing non-line-of-sight (NLOS) propagation condition. Additionally, an ulterior process is carried out to mitigate the deviation of these measurements from the actual distance value between the devices. The results show that ML techniques are suitable to identify NLOS propagation conditions and also to mitigate the error of the estimates when there is LOS between the emitter and the receiver.
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页数:15
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