State estimation in non-linear Markov jump systems with uncertain switching probabilities

IET Control Theory and Applications

Volumn 6, Issue 5, 2012, Pages 641-650

  Zhao, S ^a   Liu, F ^a  

^a JIANGNAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

FILTERING PROCESS; GAUSSIAN PROBABILITY DENSITY FUNCTIONS; MARKOV JUMP SYSTEM; MULTIPLE MODELS; PARTICLE FILTER; PRIOR KNOWLEDGE; STANDARD DEVIATION; STATE ESTIMATION METHODS; STOCHASTIC VARIABLE; SWITCHING PROBABILITY; TRANSITION PROBABILITIES;

NONLINEAR FILTERING;

STATE ESTIMATION;

EID: 84860523334     PISSN: 17518644     EISSN: 17518652     Source Type: Journal    
DOI: 10.1049/iet-cta.2011.0333     Document Type: Article

References (23)

1. McGinnity, S., and Irwin, G.W.: ' Multiple model bootstrap filter for maneuvering target tracking ', IEEE Trans. Aerosp. Electron. Syst., 2000, 36, p. 1006-1012 10.1109/7.869522 0018-9251
2. Bar-Shalom, Y., and Li, X.R.: ' Estimation and tracking: principles, techniques and software ', (Artech House, Norwell, MA, 1993)
3. Logothetis, A., and Krishnamurthy, V.: ' Expectation maximization algorithms for MAP estimation of jump Markov linear systems ', IEEE Trans. Signal Process., 1999, 47, (8), p. 2139-2156 10.1109/78.774753 1053-587X
4. Maget, N.O., Hetreux, G., and Lann, J.M.L.: et al. ' Model-based fault diagnosis for hybrid system: application on chemical processes ', Comput. Chem. Eng., 2009, 33, (10), p. 1617-1630 10.1016/j.compchemeng.2009.04.016 0098-1354
5. Tugnait, J.K.: ' Adaptive estimation and identification for discrete systems with Markov jump parameters ', IEEE Trans. Autom. Control, 1982, 27, (5), p. 1054-1065 10.1109/TAC.1982.1103061 0018-9286
6. Blom, H., and Bar-Shalom, Y.: ' The interacting multiple model algorithm for systems with Markovian switching coefficients ', IEEE Trans. Autom. Control, 1988, 33, p. 780-783 10.1109/9.1299 0018-9286
7. Doucet, A., Gordon, N.J., and Krishamurthy, V.: ' Particle filter for state estimation of jump Markov linear systems ', IEEE Trans. Signal Process., 2001, 49, (3), p. 612-624 10.1109/78.905890 1053-587X
8. Shao, X.G., Huang, B., and Lee, J.M.: ' Constrained Bayesian state estimation - a comparative study and a new particle filter based approach ', J. Process Control, 2010, 20, p. 143-157 10.1016/j.jprocont.2009.11.002 0959-1524
9. Jilkov, V.P., and Li, X.R.: ' Online Bayesian estimation of transition probabilities for Markovian jump systems ', IEEE Trans. Signal Process., 2004, 52, (6), p. 1620-1630 10.1109/TSP.2004.827145 1053-587X
10. Orguner, U., and Demirekler, M.: ' Maximum likelihood estimation of transition probabilities of jump Markov linear systems ', IEEE Trans. Signal Process., 2008, 56, (10), p. 5093-5108 10.1109/TSP.2008.928936 1053-587X
11. Orguner, U., and Demirekler, M.: ' An online sequential algorithm for the estimation of transition probabilities for jump Markov linear systems ', Automatica, 2006, 42, (10), p. 1735-1744 10.1016/j.automatica.2006.05.002 0005-1098
12. Xiong, J.L., Lam, J., and Gao, H.J.: et al. ' On robust stabilization of Markovian jump systems with uncertain switching probabilities ', Automatica, 2005, 41, p. 897-903 10.1016/j.automatica.2004.12.001 0005-1098
13. Karan, M., Shi, P., and Koya, C.Y.: ' Transition probability bounds for the stochastic stability robustness of continuous- and discrete-time Markovian jump linear systems ', Automatica, 2006, 42, p. 2159-2168 10.1016/j.automatica. 2006.07.002 0005-1098
14. ∞ filtering for discrete-time Markovian jump linear systems with partly unknown transition probabilities ', Automatica, 2009, 45, (6), p. 1462-1467 10.1016/j.automatica.2009.02.002 0005-1098
15. ∞ model reduction for discrete-time Markov jump linear systems with partially known transition ', Int. J. Control, 2009, 82, (2), p. 243-351 10.1080/ 00207170802098899 0020-7179
16. Blom, H., and Bloem, E.: ' Eaxact Bayesian and particle filtering of stochastic hybrid systems ', IEEE Trans. Aerosp. Electron. Syst., 2007, 43, (1), p. 55-69 10.1109/TAES.2007.357154 0018-9251
17. Doucet, A., and Ristic, B.: ' Recursive State estimation for multiple switching models with unknown transition probabilities ', IEEE Trans. Aerosp. Electron. Syst., 2002, 38, (3), p. 1098-1104 10.1109/TAES.2002.1039427 0018-9251
18. Gupta, R.D., and Gupta, R.C.: ' Analyzing skewed data by power normal model ', Test, 2008, 12, (2), p. 501-524 10.1007/s11749-006-0030-x 0143-2397
19. Chze, E.S., and Inseok, H.: ' State estimation for stochastic linear hybrid systems with continuous state dependent transitions: an IMM approach ', IEEE Trans. Aerosp. Electron. Syst., 2009, 45, p. 376-391 10.1109/TAES.2009. 4805286 0018-9251
20. ∞ estimation for discrete-time piecewise homogeneous Markov jump linear systems ', Automatica, 2009, 45, p. 2570-2576 10.1016/j.automatica.2009.07.004 0005-1098
21. Boloc, M., Djuric, M., and Hong, S.: ' Resampling algorithms for particle filter: a computational complexity perspective ', J. Appl. Signal Process, 2004, 15, p. 2267-2277 10.1155/S1110865704405149
22. ∞ fuzzy filter design for uncertain nonlinear singularly perturbed systems with Markovian jumps: an LMI approach ', Inf. Sci., 2007, 177, p. 1699-1714 10.1016/j.ins.2006.10.006
23. Luan, X., Liu, F., and Shi, P.: ' Finite-time filtering for non-linear stochastic systems with partially known transition jump rates ', IET Control Theory Appl., 2009, 4, (5), p. 735-745 10.1049/iet-cta.2009.0014