Prediction, filtering, smoothing and deconvolution in a discrete H∞ infinity setting: A game theory approach

International Journal of Control

Volumn 70, Issue 6, 1998, Pages 841-857

  Kim, Hansil ^a   Jalali, Ali A ^b   Sims, Craig S ^b   Kim, Young Chul ^c  

^a University of Ulsan   (South Korea)

^b West Virginia University   (United States)

^c Chungbuk National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DISCRETE TIME CONTROL SYSTEMS; GAME THEORY; KALMAN FILTERING; LINEAR SYSTEMS; OPTIMIZATION; SIGNAL NOISE MEASUREMENT;

DISCRETE TIME LINEAR SYSTEM; OPTIMAL FILTERING ALGORITHM;

OPTIMAL CONTROL SYSTEMS;

EID: 0032140429     PISSN: 00207179     EISSN: 13665820     Source Type: Journal    
DOI: 10.1080/002071798221993     Document Type: Article

References (22)

1. Banavar, R., and Speyer, J. L., 1991, A linear-quadratic game approach to estimation and smoothing. American Control Conference, Boston, MA.
2. ∞ Optimal Control and Belated Minimax Design Problems—a Dynamic Game Approach (Halsted Press).
3. Bryson, A. E., and Ho, Y. C., 1975, Applied Optimal Control (Ginn & Company).
4. ∞ control problem. IEEE Transactions on Automatic Control, 34, 831-847.
5. ∞ filtering in multi-variable discretetime systems. IEEE International Symposium Circuits and Systems, Finland, June 1988.
6. ∞-Controlled Theory (New York: Springer-Verlag).
7. ∞ optimality criterion. SIAM Journal of Control and Optimization, 25, 815-844.
8. Grewal, M. S., and Andrews, A. P., 1993, Kalman Filtering Theory and Practice (Prentice-Hall).
9. Grimble, M. J., 1987, Minimax design of optimal linear filters. Presented at the MTNS Conference, Arizona.
10. ∞ design of optimal digital linear filters, Presented at the IEEE Colloquial Digital Signal Processing, London.
11. Houpis, C. H., and Lamont, G. B., 1992, Digital Control Systems Theory, Hardware, Software (McGraw-Hill).
12. KalMAn, R. E., and Bucy, R. S., 1961, New results in linear filtering and prediction theory. Trans. on AS ME (Journal of Basic Engineering), 83D, 95-108.
13. Lewis, F. L., 1986, Optimal Estimation with an Introduction to Stochastic Control Theory (Chichester: Wiley).
14. Meditch, J. S., 1969, Stochastic Optimal Linear Estimation and Control (McGraw-Hill).
15. Mendel, J. M., 1977, White-noise estimators for seismic data processing in oil exploration. IEEE Transactions on Automatic Control, 22, 694-706.
16. ∞ setting. IEEE Transactions on Automatic Control, 36, 152-166.
17. Nagpal, K. M., Helmick, R. E., and Sims, C. S., 1989, Innovation approach to reduced-order estimation of complementary states. International Journal of Systems Science, 20, 1173-1212.
18. Shaked, U., and Theodor, Y., 1992, A frequency domain approach to the problems of Hoo-minimum error state estimation and deconvolution. IEEE Transactions on Signal Processing, 40, 3001-3011.
19. ∞ Control Problem: A State Space Approach (Englewood Cliffs, NJ: Prentice Hall).
20. Stubberud, A. R., 1967, Optimal filtering for Gauss-Markov noise, Aerospace Report. No. TR-0158 (3307-01)-10, December, 1967.
21. ∞ optimal linear control and filtering. IEEE Transactions on Automatic Control, 36, 1264-1271.
22. Zames, G., 1981, Feedback and optimal sensitivity: model reference transformations, multiplicative seminorms, and approximate inverses, IEEE Transactions on Automatic Control, 26, 301-320.