A Handheld Inertial Pedestrian Navigation System With Accurate Step Modes and Device Poses Recognition

Author

Hemin Zhang ; Weizheng Yuan ; Qiang Shen ; Tai Li ; Honglong Chang

Author_Institution

Key Lab. of Micro & Nano Syst. for Aerosp., Northwestern Polytech. Univ., Xi´an, China

Volume

15

Issue

3

fYear

2015

fDate

Mar-15

Firstpage

1421

Lastpage

1429

Abstract

In this paper, a handheld inertial pedestrian navigation system (IPNS) based on low-cost microelectromechanical system sensors is presented. Using the machine learning method of support vector machine, a multiple classifier is developed to recognize human step modes and device poses. The accuracy of the selected classifier is >85%. A novel step detection model is created based on the results of the classifier to eliminate the over-counting and under-counting errors. The accuracy of the presented step detector is >98%. Based on the improvements of the step modes recognition and step detection, the IPNS realized precise tracking using the pedestrian dead reckoning algorithm. The largest location error of the IPNS prototype is ~40 m in an urban area with a 2100-m-long distance.

Keywords

computerised instrumentation; inertial navigation; learning (artificial intelligence); microsensors; pattern classification; pedestrians; support vector machines; IPNS; device poses. recognize; distance 2100 m; handheld inertial pedestrian navigation system; human step mode recognition; machine learning method; microelectromechanical system sensor; multiple classifier; overcounting error elimination; pedestrian dead reckoning algorithm; step detection model; support vector machine; undercounting error elimination; Band-pass filters; Legged locomotion; Magnetic sensors; Magnetometers; Navigation; Training; Pedestrian navigation; dead reckoning; machine learning; microsensors; motion recognition;

fLanguage

English

Journal_Title

Sensors Journal, IEEE

Publisher

ieee

ISSN

1530-437X

Type

jour

DOI

10.1109/JSEN.2014.2363157

Filename

6924767