Journal of Guangdong University of Technology ›› 2017, Vol. 34 ›› Issue (05): 15-21.doi: 10.12052/gdutxb.170080

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A Fingerprint Matching Localization Method Based on Deep Auto Encoder

Liu Zhen-yu, Li Jia-jun, Wang Kun   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2017-04-06 Online:2017-09-09 Published:2017-07-10

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Abstract: Indoor localization methods of simple structure are hard to construct a complex indoor signal model, which leads to a poor position result. To solve this problem, a RFB-SDAE algorithm is proposed. The algorithm uses a deep learning structure to establish a deep nonlinear learning model to improve the indoor position accuracy. It removes the redundant information from fingerprint acquisition by the method of simplification and preprocessing. Via the pertaining and parameter adjustment of the networks of stacked denoising auto encoder, it acquires a more exact indoor wireless signal model which is used for indoor localization. The experimental result shows that, compared with stacked denoising auto encoder (SDAE) and single neural network, the RFB-SDAE has improved its accuracy and shortened its execution time.

Key words: indoor localization, deep learning, auto encoder, fingerprint matching, reduction

CLC Number: 

  • TN911.23
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