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Foot-mounted Pedestrian Navigation based on Particle Filter with an Adaptive Weight Updating Strategy

Published online by Cambridge University Press:  31 July 2014

Yang Gu ,
Qian Song ,
Yanghuan Li  and
Ming Ma
Yang Gu*
Affiliation:
(National University of Defence Technology)
Qian Song
Affiliation:
(National University of Defence Technology)
Yanghuan Li
Affiliation:
(National University of Defence Technology)
Ming Ma
Affiliation:
(National University of Defence Technology)
*
(E-mail: sunwheat1990@126.com)
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Abstract

The algorithm flow of an inertial-based Pedestrian Navigation System (PNS) can be divided into a trajectory-generation stage and trajectory-calibration stage. The Zero-velocity UPdaTe (ZUPT)-aided Extended Kalman Filter (EKF) algorithm is commonly used to resolve the trajectory of a walking person, but it still suffers from long-term drift. Many methods have been developed to suppress these drifts and thus to calibrate the trajectory generated by the previous stage. However, these methods have certain requirements, such as explicit map information or frequent location revisits, which are hard to satisfy in such situations as Search and Rescue (SAR) operations. A new approach is proposed in this paper that requires no explicit presupposition. This approach is based on a particle filter framework, with the weight of particles being adaptively adjusted according to the a priori knowledge of building structures and human behaviours. The distribution of particle weights is designed with awareness of the regular structures of buildings. The time-varying parameter of the distribution is acquired from a Hidden Markov Model (HMM) based on the foregoing odometry, which has a close relation with human behaviour. HMM is trained offline based on samples acquired in advance. Many real-world experiments under various scenarios were performed, and the results indicate good accuracy and robustness of the proposed approach.

Keywords

Inertial-based PNSParticle filterA priori knowledgeHMM

Information

Type
Research Article
Information
The Journal of Navigation , Volume 68 , Issue 1 , January 2015 , pp. 23 - 38
Copyright
Copyright © The Royal Institute of Navigation 2014 

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