Indoor Location Algorithm Combining CNN and WiFi Fingerprint Database
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    Abstract:

    In order to improve the accuracy of WiFi-based indoor positioning and reduce the calculating time, this study proposes an indoor location algorithm combining Convolutional Neural Networks (CNN) with traditional fingerprint library. Based on the Received Signal Strength Indication (RSSI) data, the algorithm first uses the CNN model to predict the initial position of the measured point according to the real-time input data. Under the premise that the large-scale prediction position is guaranteed to be correct, the fingerprint points in the traditional fingerprint database are combined to determine the final prediction position with higher accuracy. The results show that the location accuracy of the error within 1 m is about 65%, the location accuracy of the error within 1.5 m is about 85%, and the error is stable under the premise that the timeliness is required.

    Reference
    [1] Jung SH, Moon BC, Han D. Unsupervised learning for crowdsourced indoor localization in wireless networks. IEEE Transactions on Mobile Computing, 2016, 15(11): 2892-2906. [doi: 10.1109/TMC.2015.2506585
    [2] Liu H, Darabi H, Banerjee P, et al. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2007, 37(6): 1067-1080. [doi: 10.1109/TSMCC.2007.905750
    [3] Mainetti L, Patrono L, Sergi I. A survey on indoor positioning systems. Proceedings of the 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM). Split, Croatia. 2015. 111-120.
    [4] Nowicki M, Wietrzykowski J. Low-effort place recognition with WiFi fingerprints using deep learning. arXiv: 1611.02049, 2016.
    [5] Yu YL, Liu FX, Mao S. Fingerprint extraction and classification of wireless channels based on deep convolutional neural networks. Neural Processing Letters, 2018, 48(3): 1767-1775. [doi: 10.1007/s11063-018-9800-1
    [6] Zhang W, Liu K, Zhang WD, et al. Deep neural networks for wireless localization in indoor and outdoor environments. Neurocomputing, 2016, 194: 279-287. [doi: 10.1016/j.neucom.2016.02.055
    [7] Nuño-Maganda MA, Herrera-Rivas H, Torres-Huitzil C, et al. On-device learning of indoor location for WiFi fingerprint approach. Sensors, 2018, 18(7): 2202. [doi: 10.3390/s18072202
    [8] Yang L, Fan CX, Li Y, et al. Improving deep neural network with multiple parametric exponential linear units. Neurocomputing, 2018, 301: 11-24. [doi: 10.1016/j.neucom.2018.01.084
    [9] Ferreira BV, Carvalho E, Ferreira MR, et al. Exploiting the use of convolutional neural networks for localization in indoor environments. Applied Artificial Intelligence, 2017, 31(3): 279-287
    [10] North MA. A method for implementing a statistically significant number of data classes in the jenks algorithm. Proceedings of the 6th International Conference on Fuzzy Systems and Knowledge Discovery. Tianjin, China. 2009. 35-38.
    [11] Wu B, Ma ZX, Poslad S, et al. An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination. Proceedings of IEEE 2018 Wireless Telecommunications Symposium (WTS). Phoenix, AZ, USA. 2018. 1-8.
    [12] Lee YC, Park SH. RSSI-based fingerprint map building for indoor localization. Proceedings of the 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). Jeju, Republic of Korea. 2013. 292-293.
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曹建荣,张旭,武欣莹,吕俊杰,杨红娟.结合CNN和WiFi指纹库的室内定位算法.计算机系统应用,2020,29(7):173-179

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History
  • Received:December 05,2019
  • Revised:January 03,2020
  • Online: July 04,2020
  • Published: July 15,2020
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