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Sensors 2019, 19(2), 294; https://doi.org/10.3390/s19020294

Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System

1
Internet of Things Engineering, Jiangnan University, Wuxi 214000, China
2
Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 23 November 2018 / Revised: 7 January 2019 / Accepted: 8 January 2019 / Published: 12 January 2019
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: indoor inertial positioning; MEMS-IMU; improved pedestrian dead reckoning; robust adaptive Kalman filter indoor inertial positioning; MEMS-IMU; improved pedestrian dead reckoning; robust adaptive Kalman filter
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Fan, Q.; Zhang, H.; Pan, P.; Zhuang, X.; Jia, J.; Zhang, P.; Zhao, Z.; Zhu, G.; Tang, Y. Improved Pedestrian Dead Reckoning Based on a Robust Adaptive Kalman Filter for Indoor Inertial Location System. Sensors 2019, 19, 294.

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