5G indoor location algorithm based on Chan-Taylor and optimized BP neural network

被引：0
|
作者：
Li S. [1 ,2 ]
Wu J. [1 ,3 ]
机构：
[1] National Time Service Center, Chinese Academy of Sciences, Xi’an
[2] School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing
[3] School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing
来源：
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2023年 / 31卷 / 08期
关键词：
5G positioning; BP neural network; Chan-Taylor algorithm; GA-BP neural network; indoor positioning; time difference of arrival;
D O I：
10.13695/j.cnki.12-1222/o3.2023.08.009
中图分类号：
学科分类号：
摘要：
To improve the accuracy of 5G indoor positioning in complex environments, a 5G indoor positioning algorithm based on Chan-Taylor and optimized BP neural network is designed for different application scenarios. When no samples are available, the fusion Chan-Taylor algorithm is proposed, and the Chan algorithm is used to calculate the localization value as the initial value of the Taylor algorithm for iterative calculation; When a small number of samples are available, BP neural network is more effective; When a large number of samples are available, genetic algorithm is used to improve the BP neural network to improve positioning accuracy. Comparative experiments are conducted on three algorithms in different scenarios, and the experimental results show that the Chan-Taylor algorithm has better robustness and applicability when no samples are available; In the case of 45 samples training, BP has the highest positioning accuracy of 0.3649 m; In the case of 400 samples training, GA-BP has the highest positioning accuracy. © 2023 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.
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页码：806 / 813and822
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