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

被引：27

作者：

Fan, Qigao ^{[1
]}

Zhang, Hai ^{[1
]}

Sun, Yan ^{[1
]}

Zhu, Yixin ^{[1
]}

Zhuang, Xiangpeng ^{[1
]}

Jia, Jie ^{[1
]}

Zhang, Pengsong ^{[1
]}

机构：

[1] Jiangnan Univ, Coll Internet Things Engn, Wuxi 214000, Peoples R China

来源：

SENSORS | 2018年 / 18卷 / 05期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

pedestrian positioning; attitude fusion filter; zero velocity update algorithm; OEKF; PERSPECTIVE; NAVIGATION; ALGORITHM; LOCATION;

D O I：

10.3390/s18051404

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

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.

引用

页数：15

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