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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
  • 44 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

References

  1. 1.
    Zhou Z, Li Y, Liu J et al (2013) Equality constrained robust measurement fusion for adaptive Kalman-filter-based heterogeneous multi-sensor navigation. IEEE Trans Aerosp Electron Syst 49(4):2146–2157CrossRefGoogle Scholar
  2. 2.
    Guo C, Xu B, Tian Z (2018) Research on multi-constellation GNSS compatible acquisition strategy based on GPU high-performance operation. EURASIP J Wirel Commun Netw 1:112CrossRefGoogle Scholar
  3. 3.
    Liu L, Fu J (2010) Improved state-χ2 fault detection of navigation systems based on neural network. In: Control and decision conference (CCDC), 2010 Chinese. IEEE, pp 3932–3937Google Scholar
  4. 4.
    Liu HY, Feng CT, Wang HN (2011) Method of inertial aided satellite navigation and its integrity monitoring. J Astronaut 32(4):775–780Google Scholar
  5. 5.
    Zhenlu S (1999) Optimal fault detection filter design. J Beijing Univ Aeronaut AstronautGoogle Scholar
  6. 6.
    Tao H (2008) Application of BP neural network in sensor fault diagnosis for flight control system. Comput Meas Control 16(5):613–615Google Scholar
  7. 7.
    Wei-Guang G (2008) Neural network aided GPS/INS integrated navigation fault detection algorithms. Acta Geod Cartogr Sin 80(2):186–192Google Scholar
  8. 8.
    Zhang J, Zhang T, Jiang X et al (2012) Tightly coupled GPS/INS integrated navigation algorithm based on Kalman filter. In: International conference on business computing & global informatization. IEEE, pp 588–591Google Scholar
  9. 9.
    Zhang Y, Wang H, Wang H (2017) Integrated navigation positioning algorithm based on improved Kalman filter. In: International conference on smart grid and electrical automation. IEEE, pp 255–259Google Scholar
  10. 10.
    Hongxin J, Tao Y, Xiaogang W et al (2017) Unmanned aerial vehicle relative navigation method based on robust high degree cubature filtering. J Natl Univ Def Technol 39(4):139–143Google Scholar
  11. 11.
    Li-Jia XU, Yang-Zhou C, Ping-Yuan C (2004) State estimation of integrated navigation system based on neural network. J Chin Inert Technol 12(2):40–46Google Scholar
  12. 12.
    Wang M, Fu Y (2008) State estimation of ALV integrated navigation system based on BP neural network. In: Eighth international conference on intelligent systems design & applications. IEEEGoogle Scholar
  13. 13.
    Chen X, Xiang S, Liu CL, Pan CH (2014) Vehicle detection in satellite images by hybrid deep convolutional neural networks. IEEE Geosci Remote Sens Lett 11(10):1797–1801CrossRefGoogle Scholar
  14. 14.
    Li K, Wu Y, Nan Y et al (2017) Hierarchical multi-class classification in multimodal spacecraft data using DNN and weighted support vector machine. Neurocomputing 259:55–65CrossRefGoogle Scholar
  15. 15.
    Kai-Wei C, Chang HW, Li CY et al (2009) An artificial neural network embedded position and orientation determination algorithm for low cost MEMS INS/GPS integrated sensors. Sensors 9(4):2586CrossRefGoogle Scholar
  16. 16.
    Li YC, Lu XL, Wang HX, Bao Z (2007) Research on positioning and measuring speed in the high speed sar system based on high precision map matching. Syst Eng ElectronGoogle Scholar
  17. 17.
    Yan X, Ouyang Y, Sun F et al (2013) Kalman filter applied in underwater integrated navigation system. Geod Geodyn 4(1):46–50CrossRefGoogle Scholar
  18. 18.
    Guo C, Yan J, Tian Z (2018) Analysis and design of an attitude calculation algorithm based on Elman neural network for SINS. Clust Comput 6:1–6Google Scholar
  19. 19.
    Yin A, Lu J, Dai Z et al (2016) Isomap and deep belief network-based machine health combined assessment model. Stroj Vesn 62(12):740–750CrossRefGoogle Scholar
  20. 20.
    Hemmati F, Orfali W, Gadala MS (2016) Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation. Appl Acoust 104:101–118CrossRefGoogle Scholar
  21. 21.
    Hauberg S (2015) Principal curves on Riemannian manifolds. IEEE Trans Pattern Anal Mach Intell 38(9):1915CrossRefGoogle Scholar
  22. 22.
    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. MIT Press, CambridgeCrossRefzbMATHGoogle Scholar
  23. 23.
    Jafari A, Almasganj F (2010) Using Laplacian eigenmaps latent variable model and manifold learning to improve speech recognition accuracy. Speech Commun 52(9):725–735CrossRefGoogle Scholar
  24. 24.
    Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    AlThobiani F, Ball A (2014) An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Syst Appl 41(9):4113–4122CrossRefGoogle Scholar
  27. 27.
    Fu S, Wu J, Wen H, Cai Y, Wu B (2018) Software defined wireline-wireless cross-networks: framework, challenges, and prospects. IEEE Commun Mag 56(8):145–151CrossRefGoogle Scholar

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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