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An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

College of Internet of Things Engineering, Jiangnan University, Wuxi 214000, China
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Sensors 2018, 18(5), 1404; https://doi.org/10.3390/s18051404
Received: 20 March 2018 / Revised: 27 April 2018 / Accepted: 28 April 2018 / Published: 2 May 2018
(This article belongs to the Section Physical Sensors)
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Abstract

Aimed at overcoming the problems of cumulative errors and low positioning accuracy in single Inertial Navigation Systems (INS), an Optimal Enhanced Kalman Filter (OEKF) is proposed in this paper to achieve accurate positioning of pedestrians within an enclosed environment. Firstly, the errors of the inertial sensors are analyzed, modeled, and reconstructed. Secondly, the cumulative errors in attitude and velocity are corrected using the attitude fusion filtering algorithm and Zero Velocity Update algorithm (ZUPT), respectively. Then, the OEKF algorithm is described in detail. Finally, a pedestrian indoor positioning experimental platform is established to verify the performance of the proposed positioning system. Experimental results show that the accuracy of the pedestrian indoor positioning system can reach 0.243 m, giving it a high practical value. View Full-Text
Keywords: pedestrian positioning; attitude fusion filter; zero velocity update algorithm; OEKF pedestrian positioning; attitude fusion filter; zero velocity update algorithm; OEKF
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Fan, Q.; Zhang, H.; Sun, Y.; Zhu, Y.; Zhuang, X.; Jia, J.; Zhang, P. An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model. Sensors 2018, 18, 1404.

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