Research Article
RFID Indoor Location Based on Optimized Generalized Regression Neural Network
@INPROCEEDINGS{10.1007/978-3-030-32388-2_14, author={Fangjin Chen and Xiangmao Chang and Xiaoxiang Xu and Yanjun Lu}, title={RFID Indoor Location Based on Optimized Generalized Regression Neural Network}, proceedings={Machine Learning and Intelligent Communications. 4th International Conference, MLICOM 2019, Nanjing, China, August 24--25, 2019, Proceedings}, proceedings_a={MLICOM}, year={2019}, month={10}, keywords={RFID Generalized regression neural network Indoor location}, doi={10.1007/978-3-030-32388-2_14} }- Fangjin Chen
Xiangmao Chang
Xiaoxiang Xu
Yanjun Lu
Year: 2019
RFID Indoor Location Based on Optimized Generalized Regression Neural Network
MLICOM
Springer
DOI: 10.1007/978-3-030-32388-2_14
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.