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Waist mounted Pedestrian Dead-Reckoning system

Authors: null Jaehyun Park; null Yunki Kim; null Jangmyung Lee;

Waist mounted Pedestrian Dead-Reckoning system

Abstract

This paper proposes a waist mounted PDR(Pedestrian Dead-Reckoning) algorithm using a low cost MEMS IMU(Inertial Measurement Unit). The PDR algorithm is consist of three algorithms which are step detection, step length estimation and heading estimation. The step detection is to detect a gait of pedestrian in walking. The step length estimation is to estimate distance of walking. The heading estimation is to find direction of walking. The PDR scheme divides two methods depending on position of mounted IMU where foot or waist mainly. This paper uses waist mounted PDR for convenience of easy implementation. Peak detection and zero crossing method are used for detecting step using 3D accelerometer data. Step length estimation based on non-linear model is applied and HDR algorithm is used for estimating the heading. To verify the effectiveness of this system, real-time system is implemented and demonstrated. Experimental results show accuracy of below 3% position error.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
12
Top 10%
Top 10%
Average
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