Title: A neural network enhanced detection method for GPS with slowly growing error

Authors: Weina Chen; Zhong Yang; Yizhi Wang; Shanshan Gu; Yujuan Tang

Addresses: College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, China ' College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, China ' College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, China ' College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, China ' College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, China

Abstract: The slowly growing error in GPS positioning is the difficult type of failure to detect. To solve the problem, an improved detection method based on the neural network has been proposed and implemented in the study. It is found that there is a certain mapping relationship between the constructed test statistics and the fault. A synthesis method of the back propagation neural network model for fault detection and the robust estimation theory for data fusion has been proposed and implemented in this study. The simulation results and the field test show that the method proposed has a better detection performance compared with other algorithms. It can shorten the time of fault detection so as to improve the accuracy and integrity of the INS/GPS system in required navigation performance.

Keywords: global positioning system; slowly growing error; integrity monitoring; neural network; integrated navigation.

DOI: 10.1504/IJMIC.2021.10048045

International Journal of Modelling, Identification and Control, 2021 Vol.39 No.2, pp.150 - 158

Received: 25 Jan 2021
Accepted: 11 May 2021

Published online: 20 Jun 2022 *

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