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Open AccessArticle

Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

by Wei Shi 1,*, Yang Wang 1 and Yuanxin Wu 2
School of Aeronautics and Astronautics, Central South University, Changsha 410083, China
Shanghai Key Laboratory of Navigation and Location based Services, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Author to whom correspondence should be addressed.
Academic Editors: Stefan Bosse, Ansgar Trächtler, Klaus-Dieter Thoben, Berend Denkena and Dirk Lehmhus
Sensors 2017, 17(2), 427;
Received: 30 November 2016 / Revised: 9 February 2017 / Accepted: 19 February 2017 / Published: 22 February 2017
(This article belongs to the Special Issue System-Integrated Intelligence and Intelligent Systems)
The foot-mounted inertial navigation system is an important method of pedestrian navigation as it, in principle, does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial pedestrian navigation aided with both ZUPT (zero velocity update) and the range decomposition constraint performs better than those in their own respective methods. This paper recommends that the separation distance between the position estimates of the two foot-mounted inertial navigation systems be restricted by an ellipsoidal constraint that relates to the maximum step length and the leg height. The performance of the proposed method is studied by utilizing experimental data, and the results indicate that the method can effectively correct the dual navigation systems’ position over the traditional spherical constraint. View Full-Text
Keywords: inertial navigation system; ZUPT; ellipsoidal constraint; correct position inertial navigation system; ZUPT; ellipsoidal constraint; correct position
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MDPI and ACS Style

Shi, W.; Wang, Y.; Wu, Y. Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering. Sensors 2017, 17, 427.

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