A positioning algorithm based on improved robust extended Kalman filter with NLOS identification and mitigation

被引:0
|
作者
Wang, Yan [1 ]
Yang, Huikang [1 ]
Gong, Yuxin [1 ]
机构
[1] Northeastern Univ, Dept Comp & Commun Engn, Qinhuangdao 066004, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Wireless sensor network; Mean reconstruction; Robust extended Kalman filter; NLOS identification; Residual weighting; TRACKING;
D O I
10.1186/s13638-023-02270-3
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 development of the information age and the maturity of Internet of Things technology, wireless sensor network has been widely applied in indoor localization. However, the non-line-of-sight (NLOS) propagation in complicated environment and the inherent noise of the sensor will introduce errors in the measurements, which will seriously lead to inaccurate positioning. In this paper, a novel localization scheme based on the mean reconstruction method is proposed, which reconstructs the distance measurements from all beacon nodes by taking the average twice to weaken the adverse effects of NLOS. At the same time, the noise average is re-estimated when the distance difference is not too large. Next, the robust extended Kalman filter (REKF) is used to process the reconstructed distance measurements to obtain positioning results. To make the positioning results more accurate, hypothesis test is used as NLOS identification to classify the position estimates generated from all distance combinations by least-squares. Then, the residual weighting (RWGH) method is utilized to combine the position estimates that fall into the validation region. At last, we merge the results from RWGH and REKF. The simulation and experimental results show that the proposed algorithm has high positioning accuracy and strong positioning robustness.
引用
收藏
页数:29
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