Optimal Filtering for Linear Discrete-Time Systems with Single Delayed Measurement

  • Zhao, Hong-Guo (Department of Information Science and Technology, Taishan University) ;
  • Zhang, Huan-Shui (School of Control Science and Engineering, Shandong University) ;
  • Zhang, Cheng-Hui (School of Control Science and Engineering, Shandong University) ;
  • Song, Xin-Min (School of Control Science and Engineering, Shandong University)
  • Published : 2008.06.30

Abstract

This paper aims to present a polynomial approach to the steady-state optimal filtering for delayed systems. The design of the steady-state filter involves solving one polynomial equation and one spectral factorization. The key problem in this paper is the derivation of spectral factorization for systems with delayed measurement, which is more difficult than the standard systems without delays. To get the spectral factorization, we apply the reorganized innovation approach. The calculation of spectral factorization comes down to two Riccati equations with the same dimension as the original systems.

Keywords

References

  1. B. D. O. Anderson and J. J. Moore, Optimal Filtering, Prentice-Hall, Englewood Cliffs, NJ, 1979
  2. T. Kailath, A. H. Sayed, and B. Hassibi, Linear Estimation, Prentice-Hall, Englewood Cliffs, NJ, 1999
  3. A. Ahlen and M. Sternad, "Wiener filter design using polynomial equations," IEEE Trans. on Signal Processing, vol. 39, no. 11, pp. 2387-2399, 1991 https://doi.org/10.1109/78.97994
  4. V. Kucera, "New results in state estimation and regulation," Automatica, vol. 17, no. 5, pp. 745-748, 1981 https://doi.org/10.1016/0005-1098(81)90021-2
  5. U. Shaked, "A general transfer function approach to linear stationary filtering and steady-state optimal control problem," Int. J. Control, vol. 25, no. 6, pp. 741-770, 1976
  6. J. Jezek and V. Kucera, "Efficient algorithm for matrix spectral factorization," Automatica, vol. 21, no. 6, pp. 663-669, 1985 https://doi.org/10.1016/0005-1098(85)90040-8
  7. A. H. Sayed and T. Kailath, "A survey of spectral factorization methods," Numerical Linear Algebra with Applications, vol. 8, pp. 467-496, 2001 https://doi.org/10.1002/nla.250
  8. B. D. O. Anderson, K. L. Hitz and N. D. Diem, "Recursive algorithm for spectral factorization," IEEE Trans. on Circuits and Systems, vol. 21, no. 6, pp. 742-750, 1974 https://doi.org/10.1109/TCS.1974.1083942
  9. M. Gevers and W. R. E. Wouters, "An innovations approach to the discrete-time stochastic realization problem," Journal A, vol. 19, pp. 90-109, 1978
  10. M. S. Briggs, "Linear filtering for time-delay system," IMA Journal of Mathematical Control and Information, vol. 6, no. 2, pp. 167-178, 1989 https://doi.org/10.1093/imamci/6.2.167
  11. H. Kwakernaak, "Optimal filtering in linear systems with time delays," IEEE Trans. on Automatic Control, vol. 12, no. 2, pp. 169-173, 1967 https://doi.org/10.1109/TAC.1967.1098541
  12. H. Zhang, L. Xie, and D. Zhang, "A reorganized innovation approach to linear estimation," IEEE Trans. on Automatic Control, vol. 49, no. 10, pp. 1810-1814, 2004 https://doi.org/10.1109/TAC.2004.835599
  13. H. Zhang, L. Xie, Y. Soh, and D. Zhang, "$H{\infty}$ fix-lag smoothing for discrete linear timevarying systems," Automatica, vol. 41, no. 5, pp. 839-846, 2005 https://doi.org/10.1016/j.automatica.2004.11.028
  14. H. Zhang and L. Xie, "Optimal and self-tuning deconvolution in time domain," IEEE Trans. on Signal Processing, vol. 47, no. 8, pp. 2253-2261, 1999 https://doi.org/10.1109/78.774768
  15. H. Zhang, X, Liu, and T. Chai, "A new method for optimal deconvolution," IEEE Trans. on Signal Processing, vol. 45, no. 10, pp. 2596-2599, 1997 https://doi.org/10.1109/78.640728