Improved State-x2 Fault Detection of Navigation Systems Based on Neural Network

摘要：

In INS /GPS Integrated Navigation Systems, the classic state- x2 testing method is used to ascertain if any fault exists by comparing a priori information with measurement results and examining whether the structure of the mean and covariance matrix of the n-DOF of Gaussian distributed random vector is consistent with the hypothetic values. A fault can be found with this method; however, it fails to tell the fault exists whether in the INS system or in the GPS part. This paper presents an improved neural network-based residual x2 testing technique to solve this problem; i.e., the output of the trained neural network is substituted for the INS system output when a fault is detected at first time, and the state- x2 testing algorithm is resumed. The simulation results show whether the fault comes from the INS system or the GPS system. Simulation experiments demonstrate its feasibility.

关键词： Fault Detection Integrated Navigation Neural Network System Fault

作者: Liansheng Liu Jing Fu

作者单位: Basic Experiment Center, Civil Aviation University of China,Tianjin,300300 Basic Experiment Center, Civil Aviation University of China,Tianjin,300300 College of Aviation Autom

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 3932-3937

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）