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Micromachines 2015, 6(6), 747-764; doi:10.3390/mi6060747

WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices

1
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
2
GNSS Research Center, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Stefano Mariani
Received: 30 May 2015 / Revised: 10 June 2015 / Accepted: 10 June 2015 / Published: 16 June 2015
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)

Abstract

This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM’s robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features. View Full-Text
Keywords: pedestrian navigation; smartphones; indoor positioning; MEMS sensors; WiFi fingerprinting; magnetic matching pedestrian navigation; smartphones; indoor positioning; MEMS sensors; WiFi fingerprinting; magnetic matching
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

Li, Y.; Zhuang, Y.; Lan, H.; Zhang, P.; Niu, X.; El-Sheimy, N. WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices. Micromachines 2015, 6, 747-764.

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