Self-tuning decoupled information fusion Wiener state component filters and their convergence

Automatica

Volumn 44, Issue 3, 2008, Pages 685-695

  Deng, Zi Li ^a   Gao, Yuan ^a   Li, Chun Bo ^a   Hao, Gang ^a  

^a HEILONGJIANG UNIVERSITY   (China)

Author keywords

Convergence in a realization; Decoupled fusion; Dynamic error system analysis method (DESA); MA innovation model; Modern time series analysis method; Multisensor information fusion; Noise variance estimation; Self tuning fusion; Stability; Wiener state filter

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; INFORMATION FUSION; MATHEMATICAL MODELS; SIGNAL FILTERING AND PREDICTION; TIME SERIES ANALYSIS;

CONVERGENCE IN A REALIZATION; DECOUPLED FUSION; DYNAMIC ERROR SYSTEM ANALYSIS METHOD (DESA); MA INNOVATION MODELS; MODERN TIME SERIES ANALYSIS METHOD; MULTISENSOR INFORMATION FUSION; NOISE VARIANCE ESTIMATION; SELF TUNING FUSION; WIENER STATE FILTER;

SENSOR DATA FUSION;

EID: 39449135434     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2007.07.008     Document Type: Article

References (22)

1. Anderson B.D.O., and Moore J.B. Optimal filtering (1979), Prentice-Hall, Englewood Cliffs, NJ
2. Chui C.K., and Chen G. Kalman filtering with real-time applications (1987), Springer, Berlin, Heidelberg
3. Deng Z.L. Time-domain approaches to multichannel optimal deconvolution. International Journal of Systems Science 31 6 (2000) 787-796
4. Deng Z.L., Gao Y., Mao L., Li Y., and Hao G. New approach to information fusion steady-state Kalman filtering. Automatica 41 10 (2005) 1695-1707
5. Deng Z.L., Gao Y., and Zhang M.B. Self-tuning fusion Wiener filter for ARMA signals. Science Technology and Engineering 4 9 (2004) 749-752
6. Deng Z.L., and Liu Y.M. Descriptor Kalman estimators. International Journal of Systems Science 30 11 (1999) 1205-1212
7. Deng Z.L., Ma J.W., and Gao Y. Two-sensor self-tuning information fusion Kalman filter. Science Technology and Engineering 3 4 (2003) 321-323
8. Deng Z.L., and Xu Y. Descriptor Wiener state estimators. Automatica 36 (2000) 1761-1766
9. Deng Z.L., Zhang H.S., Liu S.J., and Zhou L. Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems. Automatica 32 2 (1996) 199-216
10. Fung P.T.K., and Grimble M.J. Dynamic ship positioning using a self-tuning Kalman filter. IEEE Transactions on Automatic Control AC-28 (1983) 339-349
11. Gevers M., and Wouters W.R.E. An innovations approach to the discrete-time realization problem. Quarterly Journal on Automatic 19 (1978) 90-109
12. Hagander P., and Wittenmark B. A self-tuning filter for fixed-lag smoothing. IEEE Transactions on Information Theory IT-23 (1977) 377-384
13. Kailath T. Linear system (1980), Prentice-Hall, Englewood Cliffs, NJ
14. Karlin, S., & Taylor, H. M. (1975). A first course in stochastic processes (pp. 371-380). New York: Academic Press, Inc.
15. Ljung L. System identification: Theory for the user. (2nd ed.) (1999), Prentice-Hall PTR, Englewood Cliffs, NJ
16. Mehra R.K. On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control AC-15 2 (1970) 175-184
17. Moir T., and Grimble M.J. Optimal self-tuning filtering, prediction, and smoothing for discrete multivariable processes. IEEE Transactions on Automatic Control AC-29 (1984) 128-137
18. Roger A., and Johnson C.R. Matrix analysis (1985), Cambridge University Press, Cambridge
19. Rudin W. Principles of mathematical analysis. (3rd ed.) (2004), Beijing, China Machine Press, McGrawn-Hill Companies, Inc
20. Söderström T. Ergodicity results for sample covariances. Problems of Control and Information Theory 4 (1975) 131-138
21. Sun S.L. Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor. Automatica 40 8 (2004) 1447-1453
22. Sun S.L., and Deng Z.L. Multi-sensor optimal information fusion Kalman filter. Automatica 40 6 (2004) 1017-1023