Abstract
Nowadays, location-based services are common in our daily lives. Traditional Global Positioning System (GPS) location can provide real-time location function in outdoor complex environments, but it is insufficient for indoor location. There are many indoor location technologies, such as ultrasound, Zigbee, RFID and WIFI. RFID location technology has attracted the attention of researchers due to its high precision and low cost. Most existing RFID location algorithms are based on RSSI (Received Signal Strength Indicator) measurement. When converting RSSI to distance, the inaccurate estimation of the path loss parameter may lead to large error. In order to reduce the deviation, this paper proposes a new RFID location algorithm. Specifically, the RSSI of the target tag is read in different directions of the antenna, and the position information is predicted by the general regression neural network, which is optimized by the optimization algorithm. The experimental results show the efficiency of our proposed algorithm.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China (Grant No. 61672282) and the Basic Research Program of Jiangsu Province (Grant No. BK20161491).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, F., Chang, X., Xu, X., Lu, Y. (2019). RFID Indoor Location Based on Optimized Generalized Regression Neural Network. In: Zhai, X., Chen, B., Zhu, K. (eds) Machine Learning and Intelligent Communications. MLICOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-32388-2_14
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DOI: https://doi.org/10.1007/978-3-030-32388-2_14
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