An Indoor Localization Algorithm of UWB and INS Fusion based on Hypothesis Testing

被引:0
|
作者
Cheng, Long [1 ,2 ]
Shi, Yuanyuan [1 ]
Cui, Chen [1 ]
Zhou, Yuqing [1 ]
机构
[1] Northeastern Univ, Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China
[2] Northeastern Univ, Hebei Key Lab Marine Percept Network & Data Proc, Qinhuangdao 066004, Hebei, Peoples R China
来源
KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | 2024年 / 18卷 / 05期
基金
中国国家自然科学基金;
关键词
Wireless sensor network; non-line-of-sight; indoor location; fuzzy C-means; hypothesis test;
D O I
10.3837/tiis.2024.05.010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the rapid development of information technology, people's demands on precise indoor positioning are increasing. Wireless sensor network, as the most commonly used indoor positioning sensor, performs a vital part for precise indoor positioning. However, in indoor positioning, obstacles and other uncontrollable factors make the localization precision not very accurate. Ultra-wide band (UWB) can achieve high precision centimeter-level positioning capability. Inertial navigation system (INS), which is a totally independent system of guidance, has high positioning accuracy. The combination of UWB and INS can not only decrease the impact of non-line-of-sight (NLOS) on localization, but also solve the accumulated error problem of inertial navigation system. In the paper, a fused UWB and INS positioning method is presented. The UWB data is firstly clustered using the Fuzzy C-means (FCM). And the Z hypothesis testing is proposed to determine whether there is a NLOS distance on a link where a beacon node is located. If there is, then the beacon node is removed, and conversely used to localize the mobile node using Least Squares localization. When the number of remaining beacon nodes is less than three, a robust extended Kalman filter with M-estimation would be utilized for localizing mobile nodes. The UWB is merged with the INS data by using the extended Kalman filter to acquire the final location estimate. Simulation and experimental results indicate that the proposed method has superior localization precision in comparison with the current algorithms.
引用
收藏
页码:1317 / 1340
页数:24
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