Risk-sensitive filtering for jump Markov linear systems

Automatica

Volumn 44, Issue 1, 2008, Pages 109-118

  Orguner, Umut ^a   Demirekler, Mübeccel ^a  

^a MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

IMM; Jump Markov linear system; Multiple model estimation; Risk sensitive estimation

Indexed keywords

COMPUTER SIMULATION; LINEAR SYSTEMS; MARKOV PROCESSES; PARAMETER ESTIMATION; PROBABILITY; RISK ANALYSIS;

JUMP MARKOV LINEAR SYSTEMS; MULTIPLE MODEL ESTIMATION; RISK SENSITIVE ESTIMATION;

APPROXIMATION ALGORITHMS;

EID: 37549046381     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2007.04.018     Document Type: Article

References (35)

1. Ackerson G.A., and Fu K.S. On state estimation in switching environments. IEEE Transactions on Automatic Control 15 1 (1970) 10-17
2. Aggoun L., and Elliott R. Measure theory and filtering: Introduction with applications (2004), Cambridge University Press, UK
3. Bar-Shalom Y., and Li X.R. Estimation and tracking: Principles, techniques, and software (1993), Artech House, Norwell, MA
4. Blom, H. A. P. (1984). An efficient filter for abruptly changing systems. In Proceedings of the 23rd conference on decision and control (pp. 656-658).
5. Blom H.A.P., and Bar-Shalom Y. The interacting multiple model algorithm for systems with Markov switching coefficients. IEEE Transactions on Automatic Control 33 8 (1988) 780-783
6. Boers Y., and Driessen J.N. Interacting multiple model particle filter. IEE Proceedings-Radar Sonar and Navigation 150 5 (2003) 344-349
7. Costa O.L.V. Linear minimum mean square error estimation for discrete-time Markovian jump linear systems. IEEE Transactions on Automatic Control 39 8 (1994) 1685-1689
8. Costa O.L.V., and Guerra S. Robust linear filtering for discrete-time hybrid Markov linear systems. International Journal of Control 75 10 (2002) 712-727
9. Costa O.L.V., and Guerra S. Stationary filter for linear minimum mean square error estimator of discrete-time Markovian jump systems. IEEE Transactions on Automatic Control 47 8 (2002) 1351-1356
10. ∞ filter design for discrete-time Markovian jump linear systems. In Proceedings of the 42nd conference on decision and control, Hawai, USA.
11. ∞ filtering for discrete-time linear systems with Markovian jumping parameters. IEE Proceedings-F: Radar and Signal Processing 140 2 (1993) 107-113
12. ∞ filtering for Markovian jump linear systems. International Journal of Systems Science 33 11 (2002) 909-915
13. ∞ filtering for uncertain Markovian jump linear systems. International Journal of Robust and Nonlinear Control 12 (2002) 435-466
14. Dey, S., & Moore, J. B. (1995). Risk-sensitive filtering and smoothing via reference probability methods. In Proceedings of the American control conference, Seattle, Washington, USA.
15. Dey S., and Moore J.B. Risk-sensitive filtering and smoothing via reference probability methods. IEEE Transactions on Automatic Control 42 11 (1997) 1587-1591
16. Doucet A., Gordon N.J., and Krishnamurthy V. Particle filters for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing 49 3 (1999) 613-624
17. Doucet A., Logothetis A., and Krishnamurthy V. Stochastic sampling algorithms for state estimation of jump Markov linear systems. IEEE Transactions on Automatic Control 45 1 (2000) 188-202
18. Elliott R.J., Aggoun L., and Moore J.B. Hidden Markov models: Estimation and control (1994), Springer, NY, USA
19. Elliott R.J., Dufour F., and Sworder D.D. Exact hybrid filters in discrete time. IEEE Transactions on Automatic Control 41 12 (1996) 1807-1810
20. Gordon N.J., Salmond D.J., and Smith A.F.M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F: Radar and Signal Processing 140 2 (1993) 107-113
21. ∞ theories (1999), SIAM, Philadelphia
22. Jilkov V.P., and Li X.R. Online Bayesian estimation of transition probabilities for Markovian jump systems. IEEE Transactions on Signal Processing 52 6 (2004) 1620-1630
23. Johnston L.A., and Krishnamurthy V. An improvement to the interacting multiple model (IMM) algorithm. IEEE Transactions on Signal Processing 49 12 (2001) 2909-2923
24. Krishnamurthy V., and Evans J. Finite-dimensional filters for passive tracking of Markov jump linear systems. Automatica 34 6 (1998) 765-770
25. Kumar P.R., and Varaiya P. Stochastic systems: Estimation, identification and adaptive control (1986), Prentice-Hall, Englewood Cliffs, NJ
26. Li X.R., and Bar-Shalom Y. Multiple-model estimation with variable structure. IEEE Transactions on Automatic Control 41 4 (1996) 478-493
27. Liggins II M.E., Chong C.Y., Kadar I., Alford M.G., Vannicola V., and Thomopoulos S. Distributed fusion architectures and algorithms for target tracking. Proceedings of IEEE 85 1 (1997) 95-107
28. Logothetis A., and Krishnamurthy V. Expectation maximization algorithms for MAP estimation of jump Markov linear systems. IEEE Transactions on Signal Processing 47 8 (1999) 2139-2156
29. Mahmoud M.S., and Shi P. Optimal guaranteed cost filtering for Markovian jump discrete-time systems. Mathematical Problems in Engineering 2004 1 (2004) 33-48
30. Mazor E., Averbuch A., Bar-Shalom Y., and Dayan J. Interacting multiple model methods in target tracking: A survey. IEEE Transactions on Aerospace and Electronic Systems 34 1 (1998) 103-123
31. Orguner, U. (2006). Improved state estimation for jump Markov linear systems. Ph.D. Thesis, Middle East Technical University, Ankara, Turkey.
32. Shi P., Boukas E.K., and Agarwal R.K. Kalman filtering for continuous-time uncertain systems with Markovian jumping parameters. IEEE Transactions on Automatic Control 44 8 (1999) 1592-1597
33. Speyer, J. L., Fan, C., & Banavar, R. N. (1992). Optimal stochastic estimation with exponential cost criteria. In Proceedings of the 31st conference on decision and control (Vol. 2, pp. 2293-2298).
34. Tugnait J.K. Adaptive estimation and identification for discrete systems with Markov jump parameters. IEEE Transactions on Automatic Control 27 5 (1982) 1054-1065
35. Zhang Q. Optimal filtering of discrete-time hybrid systems. Journal of Optimization Theory and Applications 100 1 (1999) 123-144