Design of extended recursive Wiener fixed-point smoother and filter in discrete-time stochastic systems

Digital Signal Processing: A Review Journal

Volumn 17, Issue 1, 2007, Pages 360-370

  Nakamori, Seiichi ^a  

^a KAGOSHIMA UNIVERSITY   (Japan)

Author keywords

Covariance information; Discrete time stochastic systems; Extended recursive Wiener estimators; Fixed point smoother; Nonlinear modulation

Indexed keywords

COMPUTER SIMULATION; DISCRETE TIME CONTROL SYSTEMS; KALMAN FILTERING; NONLINEAR CONTROL SYSTEMS; SPEECH SYNTHESIS; STOCHASTIC CONTROL SYSTEMS;

FIXED POINT SMOOTHER; NONLINEAR OBSERVATION FUNCTION; OBSERVATION VECTOR; STATIONARY STOCHASTIC SYSTEMS;

DIGITAL FILTERS;

EID: 37849189123     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2006.03.004     Document Type: Article

References (21)

1. Van Trees H.L. Detection, Estimation, and Modulation Theory, Part 1 (1968), Wiley, New York
2. Nakamori S. Design of a fixed-point smoother based on an innovations theory for white Gaussian plus coloured observation noise. Int. J. Syst. Sci. 22 (1991) 2573-2584
3. Kailath T. Lectures on Linear Least-Squares Estimation (1976), Springer-Verlag, New York
4. Nakamori S. Estimation technique using covariance information with uncertain observations in linear discrete-time systems. Signal Process. 58 (1997) 309-317
5. Nakamori S. Design of a fixed-point smoother based on an innovations theory for white Gaussian plus coloured observation noise. Int. J. Syst. Sci. 22 (1991) 2573-2584
6. Sage A.P., and Melsa J.L. Estimation Theory with Applications to Communications and Control (1971), McGraw-Hill, New York
7. Hoon L.T., Sang R.W., Hee J.S., Sung Y.T., and Bae P.J. Robust extended Kalman filtering via Krein space estimation. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E87-A (2004) 243-250
8. Yeh H.D., and Huang Y.C. Parameter estimation for leaky aquifers using the extended Kalman filter, and considering model and data measurement uncertainties. J. Hydrol. 302 (2005) 28-45
9. Wang Y., and Papageorgiou M. Real-time freeway traffic state estimation based on extended Kalman filter: A general approach. Transport Res. B Methodol. 39 (2005) 141-167
10. Kailath T. A view of three decades of linear filtering theory. IEEE Trans. Inform. Theory IT-20 (1974) 146-181
11. Nakamori S. Estimation technique using covariance information in linear discrete-time systems. Signal Process. 43 (1995) 169-179
12. Nakamori S., Caballero-Aguilla R., Hermoso-Carazo A., and Linares-Perez J. Second-order polynomial estimators from non-independent uncertain observations using covariance information. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E86-A (1995) 1240-1248
13. Nakamori S. Design of estimators using covariance information in discrete-time stochastic systems with nonlinear observation mechanism. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E82-A (1999) 1292-1304
14. Nakamori S. Design of filter using covariance information in continuous-time stochastic systems with nonlinear observation mechanism. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E81-A (1998) 904-912
15. Nakamori S. Design of predictor using covariance information in continuous-time stochastic systems with nonlinear observation mechanism. Signal Process. 68 (1998) 183-193
16. S. Nakamori, Linear continuous sequential estimators using covariance information and its applications to estimation problems of air pollution levels, Ph.D. dissertation, Kyoto University, 1981
17. Nakamori S. Recursive estimation technique of signal from output measurement data in linear discrete-time systems. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E82-A (1995) 600-607
18. Nakamori S. Reduced-order estimation technique using covariance data of observed value in linear discrete-time systems. Digital Signal Process. 7 (1997) 55-62
19. Blahut R.E. Digital Transmission of Information (1990), Addison-Wesley, Reading, MA
20. Haykin S. Adaptive Filter Theory. fourth ed. (2002), Prentice Hall, Englewood Cliffs
21. Akaike H. Stochastic theory of minimal realization. IEEE Trans. Automat. Control 19 (1974) 667-674