Nonlinear state prediction by separation approach for continuous-discrete stochastic systems
Kybernetika, Tome 44 (2008) no. 1, pp. 61-74 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

The paper deals with a filter design for nonlinear continuous stochastic systems with discrete-time measurements. The general recursive solution is given by the Fokker–Planck equation (FPE) and by the Bayesian rule. The stress is laid on the computation of the predictive conditional probability density function from the FPE. The solution of the FPE and its integration into the estimation algorithm is the cornerstone for the whole recursive computation. A new usable numerical scheme for the FPE is designed. In the scheme, the separation technique based on the upwind volume method and the finite difference method for hyperbolic and parabolic part of the FPE is used. It is supposed that separation of the FPE and choice of a suitable numerical method for each part can achieve better estimation quality comparing to application of a single numerical method to the unseparated FPE. The approach is illustrated in some numerical examples.
The paper deals with a filter design for nonlinear continuous stochastic systems with discrete-time measurements. The general recursive solution is given by the Fokker–Planck equation (FPE) and by the Bayesian rule. The stress is laid on the computation of the predictive conditional probability density function from the FPE. The solution of the FPE and its integration into the estimation algorithm is the cornerstone for the whole recursive computation. A new usable numerical scheme for the FPE is designed. In the scheme, the separation technique based on the upwind volume method and the finite difference method for hyperbolic and parabolic part of the FPE is used. It is supposed that separation of the FPE and choice of a suitable numerical method for each part can achieve better estimation quality comparing to application of a single numerical method to the unseparated FPE. The approach is illustrated in some numerical examples.
Classification : 35B37, 60H10, 60H30, 65C30, 93E03, 93E10, 93E11
Keywords: stochastic systems; state estimation; nonlinear filters; Fokker –Planck equation; numerical solutions; finite volume method; finite difference method
@article{KYB_2008_44_1_a5,
     author = {\v{S}v\'acha, Jaroslav and \v{S}imandl, Miroslav},
     title = {Nonlinear state prediction by separation approach for continuous-discrete stochastic systems},
     journal = {Kybernetika},
     pages = {61--74},
     year = {2008},
     volume = {44},
     number = {1},
     mrnumber = {2405056},
     zbl = {1145.93047},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/KYB_2008_44_1_a5/}
}
TY  - JOUR
AU  - Švácha, Jaroslav
AU  - Šimandl, Miroslav
TI  - Nonlinear state prediction by separation approach for continuous-discrete stochastic systems
JO  - Kybernetika
PY  - 2008
SP  - 61
EP  - 74
VL  - 44
IS  - 1
UR  - http://geodesic.mathdoc.fr/item/KYB_2008_44_1_a5/
LA  - en
ID  - KYB_2008_44_1_a5
ER  - 
%0 Journal Article
%A Švácha, Jaroslav
%A Šimandl, Miroslav
%T Nonlinear state prediction by separation approach for continuous-discrete stochastic systems
%J Kybernetika
%D 2008
%P 61-74
%V 44
%N 1
%U http://geodesic.mathdoc.fr/item/KYB_2008_44_1_a5/
%G en
%F KYB_2008_44_1_a5
Švácha, Jaroslav; Šimandl, Miroslav. Nonlinear state prediction by separation approach for continuous-discrete stochastic systems. Kybernetika, Tome 44 (2008) no. 1, pp. 61-74. http://geodesic.mathdoc.fr/item/KYB_2008_44_1_a5/

[1] Daum F. E.: New exact nonlinear filters. In: Bayesian Analysis of Time Series and Dynamic Models (J. C. Spall, ed.), Marcel Dekker, New York 1988, pp. 199–226

[2] Higham D. J., Kloeden P. E.: Maple and Matlab for Stochastic Differential Equations in Finance. Technical Report. University of Strathclyde 2001

[3] Chang J. S., Cooper G.: A practical difference scheme for Fokker–Planck equations. J. Comput. Phys. 6 (1970), 1–16 | Zbl

[4] Moral P. del, Jacod J.: Interacting particle filtering with discrete observations. In: Sequential Monte Carlo Methods in Practice (A. Doucet, N. de Freitas, and N. Gordon, eds.), Springer-Verlag, New York 2001, pp. 43–75 | MR

[5] Jazwinski A. H.: Stochastic Processes and Filtering Theory. Academic Press, New York 1970 | Zbl

[6] Kalman R. E., Bucy R. S.: New results in linear filtering and prediction theory. J. Basic Engrg. 83 (1961), 95–108 | MR

[7] Kouritzin M. A.: On exact filters for continuous signals with discrete observations. IEEE Trans. Automat. Control 43 (1998), 709–715 | MR | Zbl

[8] Kushner H. J., Budhijara A. S.: A nonlinear filtering algorithm based on an approximation of the conditional distribution. IEEE Trans. Automat. Control 45 (2000), 580–585 | MR

[9] LeVeque R. J.: Finite Volume Methods for Hyperbolic Problems. Cambridge University Press, New York 2002 | MR | Zbl

[10] Lototsky S. V., Rozovskii B. L.: Recursive nonlinear filter for a continuous-discrete time model: Separation of parameters and observations. IEEE Trans. Automat. Control 43 (1998), 1154–1158 | MR | Zbl

[11] Mirkovic D.: $N$-dimensional Finite Element Package. Technical Report. Department of Mathematics, Iowa State University 1996

[12] Park B. T., Petrosian V.: Fokker–Planck equations of stochastic acceleration: A study of numerical methods. Astrophys. J., Suppl. Ser. 103 (1996), 255–267

[13] Press W. H., Flannery B. P., Teukolsky S. A., Vetterling W. T.: Numerical Recipes. Cambridge University Press, New York 1986 | MR | Zbl

[14] Risken H.: The Fokker–Planck Equation. Springer–Verlag, Berlin 1984 | MR | Zbl

[15] Schmidt G. C.: Designing nonlinear filters based on Daum’s theory. J. Guidance, Control and Dynamics 16 (1993), 371–376 | Zbl

[16] Sorenson H. W., Alspach D. L.: Recursive Bayesian estimation using Gaussian sums. Automatica 7 (1971), 465–479 | MR | Zbl

[17] Spencer B. F., Bergman L. A.: On the numerical solution of the Fokker–Planck equation for nonlinear stochastic systems. Nonlinear Dynamics 4 (1993), 357–372

[18] Spencer B. F., Wojtkiewicz S. F., Bergman L. A.: Some experiments with massively parallel computation for Monte Carlo simulation of stochastic dynamical systems. In: Proc. Second Internat. Conference on Computational Stochastic Mechanics, Athens 1994

[19] Šimandl M., Švácha J.: Nonlinear filters for continuous-time processes. In: Proc. 5th Internat. Conference on Process Control, Kouty nad Desnou 2002

[20] Šimandl M., Královec J.: Filtering, prediction and smoothing with Gaussian sum Rrpresentation. In: Proc. Symposium on System Identification. Santa Barbara 2000

[21] Šimandl M., Královec, J., Söderström T.: Anticipative grid design in point-mass approach to nonlinear state estimation. IEEE Trans. Automat. Control 47 (2002), 699–702 | MR

[22] Šimandl M., Královec, J., Söderström T.: Advanced point–mass method for nonlinear state estimation. Automatica 42 (2006), 1133–1145 | Zbl

[23] Šimandl M., Švácha J.: Separation approach for numerical solution of the Fokker–Planck equation in estimation problem. In: Preprints of 16th IFAC World Congress. Prague 2005

[24] Zhang D. S., Wei G. W., Kouri D. J., Hoffman D. K.: Numerical method for the nonlinear Fokker–Planck equation. Amer. Physical Society 56 (1997), 1197–1206

[25] Zorzano M. P., Mais, H., Vazquez L.: Numerical solution for Fokker–Planck equations in accelerators. Phys. D: Nonlinear Phenomena 113 (1998), 379–381 | Zbl