Indoor Fingerprint Location Algorithm Based on Convolutional Neural Network

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2025-4-6- 11

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Indoor Fingerprint Location Algorithm Based on Convolutional Neural Network
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                        WANG YingWANG Ying
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China;Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing(Southwest University), Chongqing 400715, China;College of Computer and Information Science, Southwest University, Chongqing 400715, China
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HUANG Xu-DongHUANG Xu-Dong
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China;Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing(Southwest University), Chongqing 400715, China
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GUO Song-TaoGUO Song-Tao
College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China;Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing(Southwest University), Chongqing 400715, China
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Fund Project: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.

Key words:indoor localization;magnetic field;deep learning;fingerprint localization;channel state information (CSI)

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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
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Online: November 13,2018
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