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Attitude Estimation for Satellite Fault Tolerant System Using Federated Unscented Kalman Filter

  • Bae, Jong-Hee (Department of Mechanical and Aerospace Engineering, Seoul National University) ;
  • Kim, You-Dan (Department of Mechanical and Aerospace Engineering, Seoul National University)
  • Published : 2010.06.15

Abstract

We propose a spacecraft attitude estimation algorithm using a federated unscented Kalman filter. For nonlinear spacecraft systems, the unscented Kalman filter provides better performance than the extended Kalman filter. Also, the decentralized scheme in the federated configuration makes a robust system because a sensor fault can be easily detected and isolated by the fault detection and isolation algorithm through a sensitivity factor. Using the proposed algorithm, the spacecraft can continuously perform a given mission despite navigation sensor faults. Numerical simulation is performed to verify the performance of the proposed attitude estimation algorithm.

Keywords

References

  1. Ali, J. and Fang, J. (2005). Multisensor data synthesis using federated form of unscented Kalman filtering. IEEE International Conference on Industrial Technology (ICIT 2005), Hong Kong. pp. 524-529. https://doi.org/10.1109/ICIT.2005.1600694
  2. Crassidis, J. L., Landis Markley, F., and Cheng, Y. (2007). Survey of nonlinear attitude estimation methods. Journal of Guidance, Control, and Dynamics, 30, 12-28. https://doi.org/10.2514/1.22452
  3. Edelmayer, A. and Miranda, M. (2007). Federated filtering for fault tolerant estimation and sensor redundancy management in coupled dynamics distributed systems. Mediterranean Conference on Control and Automation, Athens, Greece. https://doi.org/10.1109/MED.2007.4433915
  4. Ilyas, M., Lim, J., Lee, J. G., and Park, C. G. (2008). Federated unscented kalman filter design for multiple satellites formation flying in LEO. International Conference on Control, Automation and Systems (ICCAS 2008), Seoul, Korea. pp. 453-458. https://doi.org/10.1109/ICCAS.2008.4694683
  5. Jayaraman, P., Fischer, J., Moorhouse, A., and Lauer, M. (2006). Star tracker operational usage in different phases of the mars express mission. SpaceOps 2006 Conference, Rome, Italy.
  6. Karlgaard, C. D. and Schaub, H. (2008). Adaptive huberbased filtering using projection statistics: application to spacecraft attitude estimation. AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, Hawaii.
  7. Kerr, T. (1987). Decentralized filtering and redundancy management for multisensor navigation. IEEE Transactions on Aerospace and Electronic Systems, AES-23, 83-119. https://doi.org/10.1109/TAES.1987.313339
  8. Kim, Y. S. and Hong, K. S. (2003). Decentralized information filter in federated form. SICE Annual Conference, Fukui, Japan. pp. 2176 - 2181.
  9. Schaub, H. and Junkins, J. L. (2003). Analytical mechanics of space systems. Reston, VA: American Institute of Aeronautics and Astronautics.
  10. Simon, D. (2006). Optimal State Estimation: Kalman, H [infinity] and Nonlinear Approaches. Hoboken, NJ: Wiley-Interscience.
  11. Xu, Y. (2009). Nonlinear robust stochastic control for unmanned aerial vehicles. Journal of Guidance, Control, and Dynamics, 32, 1308-1319. https://doi.org/10.2514/1.40753

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