Intelligent active fault-tolerant system for multi-source integrated navigation system based on deep neural network

  • Chengjun GuoEmail author
  • Feng Li
  • Zhong Tian
  • Wei Guo
  • Shusen Tan
Smart Data Aggregation Inspired Paradigm & Approaches in IoT Applns


This paper proposes an intelligent active fault-tolerant system based on deep neural network. That is, an active fault-tolerant integrated navigation system is established by adding neural network to the fault-tolerant integrated navigation system based on one-class support vector machine fault detection algorithm. When there is no fault, the neural network trains each sub-filter; when there is a fault, the neural network which has been in the training state will predict the fault time data and use the neural network prediction data to replace the fault data into the main filter for fusion. It can be seen from the simulation analysis that the system can detect the fault of the navigation sub-filtering system well, and when the fault occurs, the prediction data of the neural network is used for information fusion. Simulation results show that the system can provide stable and reliable navigation under the condition of time-varying system and observation noise and complex environment.


Active fault-tolerant One-class SVM Fault detection Deep neural network State estimation 



The authors would like to thank Prof. Jingdong Yu at National Key Laboratory of Science and Technology on Communications of UESTC for help, and Prof. Long Jin and Prof. Yonglun Luo at Research Institute of Electronic Science and Technology of UESTC for the assistance. The author also wants to thank Research Institute of Electronic Science and Technology and Key Laboratory of Integrated Electronic System, Ministry of Education, for their support for this research. However, the opinions expressed in this paper are solely those of the authors.

Compliance with ethical standards

Conflict of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Research Institute of Electronic Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.National Key Laboratory of Science and Technology on CommunicationsUniversity of Electronic Science and Technology of ChinaChengduChina

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