Abstract
With the continuous development of modern communication technology, China has made considerable achievements in the field of wireless communication technology. In the process of application of modern meteorological observation technology, the maximum level of automation and information technology has been improved. Through the analysis of RFID positioning technology, this paper proposes a positioning algorithm based on the phase difference and signal strength of radio frequency signal. Aiming at the problem of low positioning accuracy of RFID system, the signal strength received by the antenna is firstly processed by Gaussian filtering. The filtered signal strength is combined with KNN algorithm and Bayesian optimization estimation for the initial positioning of the positioning tag. Then the position of the reference tag is marked and its phase difference and signal strength are collected to form the feature vector. The feature vectors of reference tags were trained in BP neural network algorithm, and the hidden layer features were extracted and input into the SVR training model. In order to improve the training speed, SMO algorithm is combined in the process of training the model. Finally, by inputting the feature vector of the pending positioning tag into the model that has been trained in advance, the position coordinates of the pending positioning tag can be calculated more accurately.
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References
Zhang, Z.L., Bao, R.G., Wang, X.F., et al.: Design of meteorological data acquisition system based on wireless sensor network. Shaanxi Meteorol. 2010(6), 6–8 (2010)
Kienle, F.: Architectures for Baseband Signal Processing. Springer, New York (2014)
Wang, X., Zhang, M., Lu, Z.: A frame breaking based hybrid algorithm for UHF RFID anti-collision. Comput. Mater. Continua 59(3), 873–883 (2019)
Han, Y., Zheng, W., Wen, G.C., et al.: Multi-rate polling: improve the performance of energy harvesting backscatter wireless networks. Comput. Mater. Continua 60(2), 795–812 (2019)
Gong, J.J.: Research on THREE-DIMENSIONAL Positioning Method of Wireless Sensor Network Based on RSSI. Nanchang Aviation University (2011)
Ni, L.M., Liu, Y.H., Lau, C.L.: LANDMARC: indoor location sensing using active RFID. Wirel. Netw. 10(6), 701–710 (2004)
Zou, X.Y., Han, F.W.: Recent neighbor improvement algorithm based on LANDMARC. J. Wuhan Univ. 59(3), 255–259 (2013)
Jin, G.Y., Lu, X.Y., Park, M.S.: An indoor localization mechanism using active RFID tag. In: IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy Computing, vol. 1, no. 4 (2006)
Zhou, J.R., Zhang, H.J., Mo, L.F.: Two-dimension localization of passive RFID tags using AOA estimation. In: Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–5 (2011)
Li, Z.Z.: Research on Autonomous Navigation System of Omnidirectional Four-Wheel Mobile Robot. Qingdao University of Technology (2018)
Patil, A., Munson, J., Wood, D., et al.: Bluebot: asset tracking via robotic location crawling asset tracking via robotic location crawling. Comput. Commun. 31(6), 1067–1077 (2008)
Sun, Y., Fan, Z.P.: RFID technology and its application in Indoor positioning. Comput. Appl. 25(5), 1205–1208 (2005)
Deng, H.F., Ma, Q.P., Zhou, S.W.: Indoor positioning using radio frequency identification (RFID) technology. Comput. Appl. 7(28), 1858–1865 (2008)
Gu, Y., Liu, K.H., Shi, W.G., et al.: RFID-based boundary virtual reference tag location algorithm. Comput. Eng. 37(6), 274–276 (2011)
Yang, L., Chen, Y.K., Li, X.Y., et al.: Tagoram: real-time tracking of mobile RFID tags to high precision using COTS devices. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, pp. 237–248 (2014)
Chen, N., Xialihaer, N., Kong, W., et al.: Research on prediction methods of energy consumption data. J. New Media 2(3), 99–109 (2020)
Qiu, L.Q., Huang, Z.Q., Liang, X.X.: Overview of RFID tag location perception technology. Res. Comput. Appl. 34(12), 3521–3526 (2017)
Hong, C.: Research on RFID Tag Positioning Algorithm Based on Unwrapping Phase. Wuhan Huazhong University of Science and Technology (2018)
Wang, Q., Wang, X.: Parameters optimization of the heating furnace control systems based on BP neural network improved by genetic algorithm. J. Internet of Things 2(2), 75–80 (2020)
Pan, N., Pan, D., Liu, Y.: The crime scene tools identification algorithm based on GVF-Harris-SIFT and KNN. Intell. Autom. Soft Comput. 25(2), 413–419 (2019)
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Li, H., Zhang, M., Shi, X., Wang, Y. (2021). Optimization of Location Technology in Meteorological Wireless Sensor Network. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1424. Springer, Cham. https://doi.org/10.1007/978-3-030-78621-2_50
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DOI: https://doi.org/10.1007/978-3-030-78621-2_50
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