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Micromachines 2017, 8(7), 225; doi:10.3390/mi8070225

Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter

1
College of Automation, Haibin Engineering University, Harbin 150001, China
2
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Received: 19 May 2017 / Revised: 14 July 2017 / Accepted: 15 July 2017 / Published: 19 July 2017
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Abstract

In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system’s accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios. View Full-Text
Keywords: map information; MEMS sensors; map aiding; map matching; auxiliary particle filter; cascade structure algorithm; indoor pedestrian navigation map information; MEMS sensors; map aiding; map matching; auxiliary particle filter; cascade structure algorithm; indoor pedestrian navigation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yu, C.; El-Sheimy, N.; Lan, H.; Liu, Z. Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter. Micromachines 2017, 8, 225.

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