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A novel indoor localization method using passive phase difference fingerprinting based on channel state information

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Listed:
  • Xiaochao Dang
  • Jiaju Ren
  • Zhanjun Hao
  • Yili Hei
  • Xuhao Tang
  • Yan Yan

Abstract

The device-free channel state information indoor fingerprint localization method may lead to phase offset errors, strong fingerprint noise and low sampling classification accuracy. In light of these characteristics, this article presents an indoor localization algorithm that is based on phase difference processing and principal component analysis. First, during the offline phase, this algorithm calculates phase differences to correct for random phase shifts and random time shifts in communication links. Second, the principal component analysis method is used to reduce the dimensionality of the denoised data and establish a robust fingerprint database. During the online phase, the algorithm trains a back-propagation neural network using the fingerprint data and determines the modelled mapping relationship between the fingerprint data and the physical localization after carrying out the phase difference correction and the principal component analysis–based dimensionality reduction. The experiments show that compared with existing fingerprint location methods, this algorithm has the advantages of significant denoising effectiveness and high localization accuracy.

Suggested Citation

  • Xiaochao Dang & Jiaju Ren & Zhanjun Hao & Yili Hei & Xuhao Tang & Yan Yan, 2019. "A novel indoor localization method using passive phase difference fingerprinting based on channel state information," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719844099
    DOI: 10.1177/1550147719844099
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    References listed on IDEAS

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    1. Boming Song & Shen Zhang & Jia Long & Qingsong Hu, 2017. "Fingerprinting Localization Method Based on TOA and Particle Filtering for Mines," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-10, October.
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