Abstract
By studying the characteristics of WIFI fingerprint signals and combining supervised learning methods in machine learning, an innovative indoor location algorithm based on Naïve Bayes and WIFI fingerprinting is presented. In the experiment, the router is selected as the generator of WIFI signal, and the RSSI fingerprint of the signal is collected to form the fingerprint library. The Naive Bayes models are used to train the data, and the server is used to calculate the position in order to realize the fast positioning of the intelligent terminal. Experiment is designed with an indoor environment including 6 positioning points, scanning interval is set to 5 s, and the learning time is set to 10 min. The experiment result shows that the system and algorithm perform well and the accuracy of positioning is higher than 80%.
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Su, H.-K., Lıao, Z.-X., Lin, C.-H., Lin, T.-M.: A hybrid indoor-position mechanism based on bluetooth and WiFi communications for smart mobile devices. In: 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB) (2015)
Chen, C., Han, Y., Chen, Y., Zhang, F., Ray Liu, K.J.: Time-reversal indoor positioning with centimeter accuracy using multi-antenna WiFi. In: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (2016)
Ismail, A.H., Kitagawa, H., Tasaki, R., Terashima, K.: WiFi RSS fingerprint database construction for mobile robot indoor positioning system. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2016)
Bisio, I., Cerruti, M., Lavagetto, F., Marchese, M., et al.: A trainingless WiFi fingerprint positioning approach over mobile devices. IEEE Antennas Wirel. Propag. Lett. 13, 832–835 (2014)
Ohta, M., Sasaki, J., Takahashi, S., Yamashita, K.: WiFi positioning system without AP locations for indoor evacuation guidance. In: 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE) (2015)
Hin, B.J., Lee, K.W., Choi, S.H., et al.: Indoor WiFi positioning system for Android-based smartphone. In: International Conference on Information and Communication Technology Convergence (2010)
Krumm, J., Harris, S., Meyers, B., et al.: Multi-camera multi-person tracking for EasyLiving. In: Proceedings of the 3rd IEEE International Workshop on Visual Surveillance, Dublin, Ireland, pp. 3–10 (2000)
Orr, R.J., Abowd, G.D.: The smart floor: mechanism for natural user identification and tracking. In: Proceedings of the Conference on Human Factors in Computing Systems, Hague, pp. 275–276 (2000)
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Chao, C., Xiaoran, M. (2018). An Innovative Indoor Location Algorithm Based on Supervised Learning and WIFI Fingerprint Classification. In: Sun, S., Chen, N., Tian, T. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2017. Lecture Notes in Electrical Engineering, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-10-7521-6_29
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DOI: https://doi.org/10.1007/978-981-10-7521-6_29
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