Citation

Fan Q, Zhang H, Pan P, Zhuang X, Jia J, Zhang P, Zhao Z, Zhu G, Tang Y. Sensors (Basel) 2019; 19(2): s19020294.

Affiliation

Internet of Things Engineering, Jiangnan University, Wuxi 214000, China. 6181915013@stu.jiangnan.edu.cn.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19020294

PMID

30642088

Abstract

Pedestrian dead reckoning (PDR) systems based on a microelectromechanical-inertial measurement unit (MEMS-IMU) providing advantages of full autonomy and strong anti-jamming performance are becoming a feasible choice for pedestrian indoor positioning. In order to realize the accurate positioning of pedestrians in a closed environment, an improved pedestrian dead reckoning algorithm, mainly including improved step estimation and heading estimation, is proposed in this paper. Firstly, the original signal is preprocessed using the wavelet denoising algorithm. Then, the multi-threshold method is proposed to ameliorate the step estimation algorithm. For heading estimation suffering from accumulated error and outliers, robust adaptive Kalman filter (RAKF) algorithm is proposed in this paper, and combined with complementary filter to improve positioning accuracy. Finally, an experimental platform with inertial sensors as the core is constructed. Experimental results show that positioning error is less than 2.5% of the total distance, which is ideal for accurate positioning of pedestrians in enclosed environment.

Language: en

Keywords

MEMS-IMU; improved pedestrian dead reckoning; indoor inertial positioning; robust adaptive Kalman filter