摘要
卫星导航系统接收机分为标量跟踪架构和矢量跟踪架构。矢量跟踪接收机的特点是采用一种中心导航滤波器实现所有通道信息的集中处理,这样可以充分利用通道之间的共享信息,提升接收机的性能。但由此带来的问题是通道之间的相互影响,当某个通道的信号被遮挡或者信号较弱时,会影响导航滤波器的正常工作,因此需要进行通道运行状态的监测。本文提出一种基于长短期记忆神经网络的通道状态监测方法,将通道的信息序列值作为神经网络的输入向量。仿真结果表明,本文提出的方法能够有效地检测故障,保证矢量跟踪接收机的定位精度。
Satellite navigation receiver has two different underlying architectures,including scalar tracking and vector tracking.The vector tracking receiver processes all the channels using a center navigation filter.This architecture could utilize the sharing information for improving the receiver performance.However,the channels will affect each other in this architecture.Channels with signal blockage or weaker signal will affect the navigation filter operation,and it is necessary for carrying out channel status monitoring.In this paper,a long short term memory-recurrent neural networks(LSTM-RNN)is proposed and applied in the channel status monitoring.The innovative sequence of the center navigation filter is employed as the input vector of the LSTM-RNN.Simulation results show that the proposed method could detect faults effectively and ensure the positioning accuracy of vector tracking receiver.
作者
朱震曙
吴盘龙
薄煜明
朱建良
ZHU Zhenshu;WU Panlong;BO Yuming;ZHU Jianliang(School of Automation,Nanjing University of Science and Technology,Nanjing,210094,China)
出处
《数据采集与处理》
CSCD
北大核心
2020年第1期181-187,共7页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(61473153)资助项目
航空科学基金(2016ZC59006)资助项目
关键词
卫星导航
矢量跟踪
长短期记忆神经网络
状态监测
global navigation satellite system
vector tracking
long short term memory-recurrent neural networks(LSTM-RNN)
status monitoring
