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Home > Archive>Volume 29, Issue S1, 2018 >63-72
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Indoor Fingerprint Location Algorithm Based on Convolutional Neural Network
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National Natural Science Foundation of China (61772432, 61772433); Natural Science Key Foundation of Chongqing (cstc2015jcyjBX0094); Fundamental Research Funds for the Central Universities of Southwest University (XDJK2016C040)

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    Abstract:

    With the popularity of wireless networks and smart devices, indoor positioning has been rapidly developed. In indoor positioning, the fingerprint-based positioning method has gradually become a research hotspot because it does not require external facilities and strong anti-interference. The development of deep learning in recent years has brought new opportunities for improving the accuracy of fingerprint positioning algorithms. This paper proposes a convolutional neural network (CNN)-based fingerprint location algorithm to improve the construction of the fingerprint database. First, the collected CSI and magnetic field data is processed through CNN, and the CNN model parameter values are used at each reference point as fingerprint. Then a probabilistic method is utilized for the final fingerprint matching. Experimental results show that the proposed positioning algorithm has better robustness and higher positioning accuracy than the traditional fingerprint positioning algorithm.

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王英,黄旭东,郭松涛.基于卷积神经网络的室内指纹定位算法.软件学报,2018,29(S1):63-72

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  • Received:May 01,2018
  • Online: November 13,2018
Links:Institute of Software, CASChina Computer FederationCASNSFC
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