Min-max Kalman filtering

Systems and Control Letters

Volumn 53, Issue 3-4, 2004, Pages 217-228

  Yaesh, I ^a   Shaked, U ^b  

^a Advanced Systems Division   (Israel)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

Kalman filter; Min max estimation; Stochastic H Filtering; Tracking

Indexed keywords

DIFFERENCE EQUATIONS; ERROR ANALYSIS; LINEAR SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MATRIX ALGEBRA; PROBLEM SOLVING; SET THEORY;

MIN-MAX ESTIMATION; RICCATI DIFFERENCE EQUATIONS; STOCHASTIC H∞ FILTERING; TRACKING;

KALMAN FILTERING;

EID: 4644366101     PISSN: 01676911     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sysconle.2004.04.008     Document Type: Article

References (26)

1. Athans M., Tse E. A direct derivation of the optimum linear filter using the maximum principle. IEEE Trans. Automat. Control. AC-12:1967;690-698.
2. Banavar R., Speyer J.L. Risk sensitive estimation and a differential game. IEEE Trans. Automat. Control. AC-39:1994;1914-1918.
3. ∞ -error bound Syst. Control Lett. 12:1988;1-8.
4. R.K. Boel, M.R. James, I.R. Petersen, Robust filtering, Proceedings of the 36th IEEE CDC, San Diego, December 1997.
5. Boyd S., Ghaoui El., Feron L., Balakrishnan V. Linear Matrix Inequalities in System and Control Theory. 1994;SIAM, Philadelphia, PA.
6. Bryson A.E., Ho Y.C. Applied Optimal Control. 1973;Wiley, New York.
7. Deb K. Multi-Objective Optimization using Evolutionary Algorithms. 2001;Wiley, Chicester.
8. S. Dey, S.I. Marcos, A framework for mixed estimation for hidden Markov models, Proceedings of the 37th IEEE CDC, Tampa, December 1998, pp. 3473-3478.
9. Dragan V., Morozan T. Mixed input-output optimization for time-varying Ito systems with state dependent noise. Dynamics of Continuous, Discrete and Impulsive Systems. 3:1997;317-333.
10. Gahinet P., Nemirovski A., Laub A.J., Chilali M. LMI Control Toolbox. 1995;The Mathworks, Inc. Natick, MA.
11. ∞ filtering of stationary continuous-time linear systems with stochastic uncertainties IEEE Trans. Automat. Control. AC-46:2001;1788-1791.
12. James M.R., Baras J.S., Elliot R.J. Risk sensitive control and dynamic games for partially observed discrete-time nonlinear systems. IEEE Trans. Automat. Control. AC-39:1994;780-792.
13. Jazwinsky A.H. Stochastic Processes and Filtering Theory. 1970;Academic Press, New York.
14. Kalman R.E. A new approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng. 82d:1960;35-45.
15. ∞ control with transients SIAM J. Control Optim. 29:1991;1373-1393.
16. Kwakernaak H. Minimax frequency domain performance and robustness optimization of linear feedback systems. IEEE Trans. Automat. Control. AC-30:1985;994-1004.
17. ∞ setting IEEE Trans. Automat. Control. AC-36:1991;152-166.
18. V. Pareto, Cours D'Economie Politique, Vol. I, II, D. Rouge, Lausanne, 1896.
19. ∞ control of linear time-varying systems a state-space approach SIAM J. Control Optim. 29:1991;1394-1413.
20. ∞ problems via convex optimization IEEE Trans. Automat. Control. 43:1998;1475-1480.
21. ∞ -minimum error state estimation of linear stationary processes IEEE Trans. Automat. Control. AC-35:1990;554-558.
22. Sorenson H.W. Kalman Filtering: Theory and Application. 1985;IEEE Press, New York.
23. Stoica A., Yaesh I. Jump Markovian-based control of wing deployment for an uncrewed air vehicle. J. Guid. Control Dynam. 25:2002;407-411.
24. K.C. Tan, Multi-Objective Evolutionary Algorithm, Version 2.00, National University of Singapore, 1999.
25. ∞ -norm sense, Proceedings of the 28th Conference on Decision and Control, Tampa, FL, December 1989, pp. 421-425.
26. Zames G. Feedback and optimal sensitivity. model reference transformations, multiplicative seminorms and approximate inverses IEEE Trans. Automat. Control. AC-26:1981;301-320.