Pedestrian Navigation Using iZES Framework for Bounding Heading Drift

Conference paper
First Online:
Part of the Communications in Computer and Information Science book series (CCIS, volume 646)

Abstract

In this paper, we describe an EKF-based framework called iZES (improved ZUPT-EKF-SINS) which is used to estimate the position and attitude of a person while walking. ZES framework consists of Zero velocity Updating algorithm, an Extended Kalman Filter and Strap-down inertial system. There is no observable variables for ZES to estimate heading and its drift, which will lead to inaccurate positioning. Therefore, on the basis of ZES, Zero Angular Rate Updating (ZARU) algorithm and Magnetic Correction (MC) methodology are considered into the framework, which is called iZES (improved ZES). The iZES PDR method was tested in several real indoor and outdoor environment with a foot-mounted IMU. Compared with ZES, iZES has a better performance that its horizontal positioning error is about 1.7 % of the total travelled distance.

Keywords

Pedestrian navigation Heading drift Magnetic correction Extended Kalman Filter iZES 
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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Beijing Key Laboratory of High Dynamic Navigation TechnologyBeijing Information Science and Technological UniversityBeijingChina

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