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Study on an intelligent fault-tolerant technique for multiple satellite configured navigation under highly dynamic conditions

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Abstract

The probability of pseudo-range information being faulty will increase when there is increased noise in the receiver due to highly dynamic conditions. In this paper, an intelligent fault-tolerant federated Kalman filter (IFFKF) is presented to solve the problem of invalid position information as a result of the uncertainty in the category and quantity of failed satellites in a multiple configured satellite system. At the same time, a corresponding intelligent fault-tolerant system structure based on a cell-level fusion mode of operation was designed. Furthermore, a fault diagnosis strategy is proposed to deal with gradually or suddenly changing faults. By monitoring the residual and the residual rate, an observation quality gene is obtained using a fuzzy logic control system, and the local filter is adaptively adjusted on-line. Consequently, any filtering divergence arising from a gradually changing fault can, to a large extent, be prevented. The results show that the proposed algorithm and structure can achieve reliable positioning by fully utilizing new multiple satellites, monitoring and managing the gradual fault, as well as detecting and isolating any sudden fault in a synchronous manner. As such, the proposed algorithm has the potential to improve the reliability of a multiple satellite configured navigation system.

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References

  1. Howell J. GNSS vulnerability and mitigation methods. In: Eleventh Air Navigation Conference, AN-Conf/11-WP/17, Montreal, Canada, 2003

  2. Howell J. Concept of using combinations of in dependent satellite navigation systems and their augmentations. In: Eleventh Air Navigation Conference, AN -Conf/11-WP/18, Montreal, Canada, 2003

  3. Mu R J, Han P X, Cui N G. Navigation device scheme and information processing method of RLV (in Chinese). Chin J Space Sci, 2009, 29: 117–123

    Google Scholar 

  4. Leppakoski H, Kuusniemi H, Takala J. RAIM and complementary Kalman filtering for GNSS reliability enhancement. In: IEEE/ION Position, Location, and Navigation Symposium, 2006. 948–956

  5. Ren D. Failure detection of dynamic systems with the state chi-square test. J Guid Control Dyn, 1994, 17: 271–277

    Article  MATH  Google Scholar 

  6. Song Z J, Jiang X W, Wang R X. Multi-observation fusion method based on fuzzy theory in spacecraft diagnosis system (in Chinese). J Harbin Inst Tech, 2003, 35: 778–780

    Google Scholar 

  7. Xia J H, Qin Y Y, Zhao C S. Application of fuzzy set theory to data fusion of the integrated navigation system (in Chinese). J Northwest Polytech Univ, 2008, 26: 636–639

    Google Scholar 

  8. Loebis D, Sutton R, Chudley J, et al. Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Eng Prac, 2004, 12: 1531–1539

    Article  Google Scholar 

  9. Escamilla P J, Mort N. A hybrid Kalman filter fuzzy logic multisensor data fusion architecture with fault tolerant characteristics. In: Proceedings of the 2001 International Conference on Artificial Intelligence, Las Vegas, NV, USA, 2001. 361–367

  10. Qin Y Y, Zhang H Y, Wang S H. Kalman Filter and Integrated Navigation Theory (in Chinese). Xi’an: Northwestern Polytechnical University Press, 1998

    Google Scholar 

  11. Mu R J, Rong S Y, Cui NG. Federated filter with strengthened FDIR capability for multiple sensor navigation system. In: IEEE Systems and Control in Aerospace and Astronautics, Harbin, China, 2006. 601–604

  12. Zhang Q, Zhang X L, Li H W, et al. Satellite selection algorithm for combined satellite receivers (in Chinese). J Beijing Univ Aeronaut Astronaut, 2007, 33: 1424–1427

    MATH  Google Scholar 

  13. Zhang G L, Zeng J, Li T, et al. Fault-tolerance designing of federal Kalman filter for gradual changing fault (in Chinese). Acta Aeronaut Astronaut Sin, 2005, 26: 743–747

    Google Scholar 

  14. Duan Z H, Cai Z X, Yu J X. An adaptive particle filter for soft fault compensation of mobile robots. Sci China Ser F-Inf Sci, 2008, 51: 2033–2046

    Article  MathSciNet  Google Scholar 

  15. Wang G D, Zhang S K, Yang R L. An adaptive outlier algorithm based on Kalman filtering for Beidou satellite passive combination navigation (in Chinese). J Electr Inf Tech, 2008, 30: 1982–1985

    Google Scholar 

  16. Wei W, He Y. Fundamentals of Intelligent Control (in Chinese). Beijing: Tsinghua University Press, 2008

    Google Scholar 

  17. Gao F Q, Tan Z Z. The passive BD/INS integrated navigation fuzzy adaptive Algorithm (in Chinese). J Astronaut, 2007, 28: 1190–1194

    Google Scholar 

  18. Zhou D F, Xi Y G, Zhang Z J. A suboptimal multiple fading extended Kalman filter (in Chinese). Acta Automat Sin, 1991, 17: 689–695

    MATH  Google Scholar 

  19. Zhang Q C, Li Y, Liu L D. Research on information fusion method in satellite multi-sensor attitude determination systems (in Chinese). J Astronaut, 2005, 26: 314–320

    Google Scholar 

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Correspondence to Xin Zhao.

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Wang, S., Zhao, X., Min, H. et al. Study on an intelligent fault-tolerant technique for multiple satellite configured navigation under highly dynamic conditions. Sci. China Inf. Sci. 54, 529–541 (2011). https://doi.org/10.1007/s11432-011-4192-0

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  • DOI: https://doi.org/10.1007/s11432-011-4192-0

Keywords

Navigation