An Indoor UWB NLOS Correction Positioning Method Based on Anchor LOS/NLOS Map

被引:3
|
作者
Wang, Qing [1 ]
Li, Zehui [1 ]
Zhang, Hao [1 ]
Yang, Yuan [1 ]
Meng, Xiaolin [2 ]
机构
[1] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Peoples R China
[2] Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China
关键词
Distance measurement; Nonlinear optics; Kalman filters; Indoor environment; Sensors; Filtering algorithms; Laser radar; Indoor positioning; line-of-sight (LOS)/non-line-of-sight (NLOS) map; ultrawideband (UWB); MOBILE LOCATION ESTIMATOR; MITIGATION; LOCALIZATION;
D O I
10.1109/JSEN.2023.3328715
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
With the increasing demand for location-based services (LBS) in daily production and life, indoor positioning has gradually become one of the current research hotspots. The most pressing challenge in indoor positioning is achieving high-precision location in complex indoor environments. Aiming at the problem that severe non-line-of-sight (NLOS) interference with ultrawideband (UWB) positioning accuracy in complex indoor environments, this article proposes a novel indoor UWB NLOS correction positioning method based on anchor LOS/NLOS map, which utilizes prior environmental information. After anchors are deployed, the LOS/NLOS map is generated using the prior environmental information. Then, the line-of-sight (LOS) anchors are selected for position solution, and the observations under the influence of the NLOS anchors are corrected. Then, the trajectory is optimized using a map-modified Kalman filtering algorithm, and it adopts a method to solve the position of two LOS anchors by using trajectory constraints, which can deal with more complex indoor environment. The experimental results show that the NLOS correction method in this article has better accuracy than the traditional filtering algorithm, especially in NLOS environment with few anchors. Compared with the standard Kalman filtering algorithm, the average positioning accuracy is improved by 69.30%, and compared with the robust Kalman filtering algorithm, the average positioning accuracy is improved by 33.77%.
引用
收藏
页码:30739 / 30750
页数:12
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