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Sensors 2013, 13(11), 15513-15531; doi:10.3390/s131115513

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

1
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2
Faculty of Engineering and Applied Science, Memorial University, St. John's, NL A1B 3X5, Canada
*
Author to whom correspondence should be addressed.
Received: 26 September 2013 / Revised: 6 November 2013 / Accepted: 7 November 2013 / Published: 13 November 2013
(This article belongs to the Section Physical Sensors)
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Abstract

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.
Keywords: indoor location sensing; Wi-Fi fingerprinting; fast normalized cross correlation; map matching; Kalman/map filtering indoor location sensing; Wi-Fi fingerprinting; fast normalized cross correlation; map matching; Kalman/map filtering
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Sun, Y.; Xu, Y.; Li, C.; Ma, L. Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing. Sensors 2013, 13, 15513-15531.

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