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Future Internet 2016, 8(2), 8; doi:10.3390/fi8020008

Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network

School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
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Academic Editor: Jose Ignacio Moreno Novella
Received: 24 December 2015 / Revised: 19 February 2016 / Accepted: 14 March 2016 / Published: 25 March 2016
(This article belongs to the Special Issue Future Mobile Computing)
View Full-Text   |   Download PDF [1860 KB, uploaded 25 March 2016]   |  

Abstract

Feature extraction methods have been used to extract location features for indoor positioning in wireless local area networks. However, existing methods, such as linear discriminant analysis and principal component analysis, all suffer from the multimodal property of signal distribution. This paper proposes a novel method, based on enhanced local fisher discriminant analysis (LFDA). First, LFDA is proposed to extract discriminative location features. It maximizes between-class separability while preserving within-class local structure of signal space, thereby guaranteeing maximal discriminative information involved in positioning. Then, the generalization ability of LFDA is further enhanced using signal perturbation, which generates more number of representative training samples. Experimental results in realistic indoor environment show that, compared with previous feature extraction methods, the proposed method reduces the mean and standard deviation of positing error by 23.9% and 33.0%, respectively. View Full-Text
Keywords: indoor positioning; wireless local area network; received signal strength; feature extraction indoor positioning; wireless local area network; received signal strength; feature extraction
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

Deng, Z.-A.; Wu, D.; Zhou, Y.; Na, Z. Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network. Future Internet 2016, 8, 8.

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