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

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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)

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.

Suggested Citation

  • Zhi-An Deng & Di Wu & Yiran Zhou & Zhenyu Na, 2016. "Enhanced Local Fisher Discriminant Analysis for Indoor Positioning in Wireless Local Area Network," Future Internet, MDPI, vol. 8(2), pages 1-11, March.
    • Handle: RePEc:gam:jftint:v:8:y:2016:i:2:p:8-:d:66480
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    References listed on IDEAS

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    1. Xiu-Zhi Cheng & Da-Rong Zhu & Shen Zhang & Ping He, 2015. "Tracking Positioning Algorithm for Direction of Arrival Based on Direction Lock Loop," Future Internet, MDPI, vol. 7(3), pages 1-11, June.
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