Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF

Micromachines

Yi Cui¹  Huimin Fu¹  Zhihua Wang¹  Yongbo Zhang¹  Yuliang Huang¹ 

[1] Research Center of Small Sample Technology, Beihang University, Beijing 100191, China;

关键词: indoor navigation;    MEMS sensors;    WiFi;    extended Kalman filter;    WKNN;   

DOI  :  10.3390/mi10030198

来源: DOAJ

【 摘 要 】

Indoor navigation has been developing rapidly over the last few years. However, it still faces a number of challenges and practical issues. This paper proposes a novel WiFi/MEMS integration structure for indoor navigation. The two-stage structure uses the extended Kalman filter (EKF) to fuse the information from WiFi/MEMS sensors and contains attitude-determination EKF and position-tracking EKF. In the WiFi part, a partition solution called “moving partition„ is originally proposed in this paper. This solution significantly reduces the computation time and enhances the performance of the traditional Weighted K-Nearest Neighbors (WKNN) method. Furthermore, the direction measurement is generated utilizing WiFi positioning results, and a “turn detection„ is implemented to guarantee the effectiveness. The navigation performance of the presented integration structure has been verified through indoor experiments. The test results indicate that the proposed WiFi/MEMS solution works well. The root mean square (RMS) position error of WiFi/MEMS is 0.7926 m, which is an improvement of 20.59% and 36.60% when compared to MEMS and WiFi alone. Besides, the proposed algorithm still performs well with very few access points (AP) available and its stability has been proven.

【 授权许可】

Unknown