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

Zhi-An Deng, Di Wu, Yiran Zhou and Zhenyu Na
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Zhi-An Deng: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Di Wu: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Yiran Zhou: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China
Zhenyu Na: School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China

Future Internet, 2016, vol. 8, issue 2, 1-11

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.

Keywords: indoor positioning; wireless local area network; received signal strength; feature extraction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2016
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