Research on an Improved RAIM Algorithm based on BP Neural network

被引：0
|
作者：
Zhong, Lunlong [1 ]
Zhao, Jing [1 ]
机构：
[1] Civil Aviat Univ China, Tianjin Key Lab Adv Signal Proc, Tianjin, Peoples R China
来源：
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年
基金：
国家重点研发计划;
关键词：
GNSS; BP Neural Network; RAIM; minor fault detection;
D O I：
10.1109/CCDC58219.2023.10326491
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Receiver autonomous integrity monitoring (RAIM) is an effective method for global satellite navigation system (GNSS) integrity monitoring, and the current problem of insufficient availability of RAIM will limit the application of satellite navigation system in the precision approach phase. In view of this situation, the positioning influence caused by satellite minor fault is first analyzed, and then an improved RAIM algorithm based on BP neural network is proposed. The traditional RAIM algorithm is improved by taking advantage of BP neural network being more sensitive to nonlinear relationships, so as to improve the detection performance of satellite minor fault and improve the navigation performance of the navigation system. Finally, the algorithm proposed in this paper is evaluated. The experimental results show that the improved RAIM algorithm based on BP neural network can significantly improve the ability of satellite minor fault detection, so that the satellite navigation system can meet the performance requirements of LPV-200, that is, improve the availability of RAIM in the precision approach phase.
引用
收藏
页码：3622 / 3627
页数：6
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