A Two-Phase UWB-Based Positioning Method for Indoor Non-Line-of-Sight Environments

被引：0
作者：
Zhou, Ning [1 ,2 ,3 ]
Liu, Qianyu [4 ]
Hancock, Craig [5 ]
Yang, Sen [6 ]
机构：
[1] NingboTech Univ, Sch Civil Engn, Ningbo 315100, Peoples R China
[2] China Univ Min & Technol, Sch Environm & Spatial Informat, Xuzhou 221116, Peoples R China
[3] Popsmart Technol Zhejiang Co Ltd, Ningbo 315000, Peoples R China
[4] NingboTech Univ, Sch Informat Sci & Engn, Ningbo 315100, Peoples R China
[5] Loughborough Univ, Sch Architecture Bldg & Civil Engn, Loughborough LE11 3TU, England
[6] Univ Nottingham Ningbo China, Dept Elect & Elect Engn, Ningbo 315100, Peoples R China
来源：
IEEE SENSORS JOURNAL | 2024年 / 24卷 / 24期
关键词：
Accuracy; Position measurement; Estimation; Sensors; Measurement uncertainty; Feature extraction; Prevention and mitigation; Delays; Pollution measurement; Noise measurement; Indoor positioning; non-line-of-sight (NLOS); sequential Monte Carlo (SMC) method; subtraction-average-based optimization (SABO); ultrawideband (UWB); NLOS IDENTIFICATION; LOCALIZATION; MITIGATION; TRACKING;
D O I：
10.1109/JSEN.2024.3481673
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
When positioning in indoor environments using ultrawideband (UWB), non-line-of-sight (NLOS) range measurements will degrade positioning accuracy if they are not solved properly. This article first reviews the existing solutions, and a novel approach named the two-phase target positioning (TPTP) algorithm is proposed. This algorithm involves a coarse positioning phase followed by a refined positioning phase. In the coarse positioning phase, the residual weighting algorithm is modified and utilized for generating the coarse position estimate, which is then used for identifying the NLOS range measurements. In the refinement phase, a joint constraint region is established to facilitate the generation of prior samples within the sequential Monte Carlo (SMC) method framework. The subtraction-average-based optimization (SABO) algorithm is employed to update samples and search for the optimal solution, ultimately achieving refined position estimation. Experimental results show the superiority of the TPTP algorithm over both classical and some state-of-the-art positioning algorithms in terms of positioning accuracy. Furthermore, the proposed positioning algorithm exhibits an affordable computational load for real-time applications.
引用
收藏
页码：41264 / 41276
页数：13
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