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ISSN: 2090-4886

International Journal of Sensor Networks and Data Communications
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Research Article

Pedestrian Indoor Navigation System Using Inertial Measurement Unit

Maryam Banitalebi Dehkordi*, Antonio Frisoli, Edoardo Sotgiu, Claudio Loconsole

Perceptual Robotics laboratory (PERCRO), Scuola Superiore Sant’ Anna, Pisa, Italy

*Corresponding Author:
Dr. Maryam Banitalebi Dehkordi
Perceptual Robotics laboratory (PERCRO)
Scuola Superiore Sant’ Anna, Pisa, Italy
Tel: +39 050 8753604
E-mail: [email protected]

Received date: September 26, 2013; Accepted date: May 26, 2014; Published date: June 02, 2014

Citation: Dehkordi MB, Frisoli A, Sotgiu E, Loconsole C (2014) Pedestrian Indoor Navigation System Using Inertial Measurement Unit. Sensor Netw Data Commun 2:115. doi:10.4172/2090-4886.1000115

Copyright: © 2014 Dehkordi MB, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This paper presents a method for an indoor pedestrian localization, based on the data that solely are measured by a foot-mounted Inertial Measurement Unit (IMU). To locate the user accurately, a comprehensive Extended Kalman Filter (EKF) with five states is developed. Five different error reduction methods are employed to estimate the errors of all five states. These error reduction methods feed EKF independently, at stance phases or different time intervals of swing phases. The navigation system is developed using the accelerometer and gyroscope measurements and without magnetometer, thus it is insensitive to the presence of metal and magnetic fields, and it is able to estimate the user’s tracked trajectory with the same accuracy in both indoor and outdoor environments. The system does not rely on the measurement from external infrastructure (e.g., RFID). To evaluate the accuracy of the system, several experimental tests are carried out over the known trajectories. Results demonstrate that the error of the estimated tracked trajectory is less than 1% of the total traveled distance.

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