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ISPRS Int. J. Geo-Inf. 2016, 5(5), 70; doi:10.3390/ijgi5050070

A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System

1
Department of Aerospace and Systems Engineering, Feng Chia University, No. 100, Wenhwa Road, Seatwen District, Taichung 407, Taiwan
2
Industrial Technology Research Institute, No. 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 310, Taiwan
3
Department of Geomatics Engineering, National Cheng-Kung University, No. 1, Daxue Road, East District, Tainan 701, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Georg Gartner, Haosheng Huang and Wolfgang Kainz
Received: 18 December 2015 / Revised: 26 April 2016 / Accepted: 10 May 2016 / Published: 17 May 2016
(This article belongs to the Special Issue Location-Based Services)
View Full-Text   |   Download PDF [6377 KB, uploaded 17 May 2016]   |  

Abstract

The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment. View Full-Text
Keywords: pedestrian dead reckoning; wearable device; multi-sensor fusion; fuzzy logic; step length estimation pedestrian dead reckoning; wearable device; multi-sensor fusion; fuzzy logic; step length estimation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lai, Y.-C.; Chang, C.-C.; Tsai, C.-M.; Huang, S.-C.; Chiang, K.-W. A Knowledge-Based Step Length Estimation Method Based on Fuzzy Logic and Multi-Sensor Fusion Algorithms for a Pedestrian Dead Reckoning System. ISPRS Int. J. Geo-Inf. 2016, 5, 70.

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