@inproceedings{a9fd02fdeacd45f09d4531508a792330,
title = "Adaptive Fault-tolerant Federated Filter with Fault Detection Method Based on Combination of LSTM and Chi-square Test",
abstract = "In order to solve the problem that the global estimation accuracy is affected by the gradual changing fault of federated filter subsystem, the features of gradual changing fault and the advantages of a long short-term memory (LSTM) neural network classification algorithm are analyzed. On this basis, a fault detection method combining residual Chi square detection algorithm with long short-term memory neural network detection method is proposed, which can effectively detect the gradual changing fault and abrupt faults of sub filters and reduce the impact of faults on global estimation accuracy. The simulation results show that this fault detection method is better than the traditional mathematical model diagnosis methods and the convolutional neural network (CNN) detection methods when the subsystem gradual changing fault occurs.",
keywords = "Integrated navigation, fault detection, long short-term memory neural network",
author = "Xuan Xiao and Jiaxin Liu",
note = "Publisher Copyright: {\textcopyright} 2021 Technical Committee on Control Theory, Chinese Association of Automation.; 40th Chinese Control Conference, CCC 2021 ; Conference date: 26-07-2021 Through 28-07-2021",
year = "2021",
month = jul,
day = "26",
doi = "10.23919/CCC52363.2021.9550501",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "3059--3064",
editor = "Chen Peng and Jian Sun",
booktitle = "Proceedings of the 40th Chinese Control Conference, CCC 2021",
address = "United States",
}