Skip to main content
Log in

Filtrage linéaire par morceaux avec petit bruit d'observation

  • Published:
Applied Mathematics and Optimization Aims and scope Submit manuscript

Abstract

We consider a piecewise linear filtering problem with small observation noise. In two different situations we construct an approximate finite-dimensional filter based on several Kalman-Bucy filters running in parallel and a procedure of tests. In the first case our work generalizes some results of Fleminget al. to more general piecewise linear dynamics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Références

  1. R. F. Bass and E. Pardoux (1987), Uniqueness for diffusions with piecewise constant coefficients, Probab. Theory Related Fields, 76:557–572.

    Google Scholar 

  2. A. Bensoussan (1987), On some approximation techniques in nonlinear filtering, Stochastic Differential Systems, Stochastic Control and Applications, W. H. Flemming and P. L. Lions, eds., IMA, vol. 10, Springer-Verlag, New York, pp. 17–31.

    Google Scholar 

  3. M. Chaleyat-Maurel and D. Michel (1984), Des résultats de non existence de filtre de dimension finie, Stochastics, 13:83–102.

    Google Scholar 

  4. N. El Karoui and M. Chaleyat-Maurel (1978), Un problème de réflexion et ses applications au temps local et aux équations différentielles stochastiques sur ℝ, cas continu, Astérisque, 52–53:117–144.

    Google Scholar 

  5. W. H. Fleming, D. Ji, and E. Pardoux (1988), Piecewise linear filtering with small observation noise, Proc. 8th INRIA Conf. on Analysis and Optimization of Systems, Lecture Notes in Control and Information Science, vol. 111, Springer-Verlag, Berlin, pp. 725–739.

    Google Scholar 

  6. W. H. Fleming, D. Ji, P. Salame, and Q. Zhang (1991), Piecewise monotone filtering in discrete time with small observation noise, IEEE Trans. Automat. Control, 36:1181–1186.

    Google Scholar 

  7. W. H. Fleming and E. Pardoux (1989), Piecewise monotone filtering with small observation noise, SIAM J. Control Optim., 20:1156–1181.

    Google Scholar 

  8. M. Fujisaki, G. Kallianpur, and H. Kunita (1972), Stochastic differential equations for the nonlinear filtering problem, Osaka J. Math., 9:19–44.

    Google Scholar 

  9. A. H. Jazwinski (1970), Stochastic Processes and Filtering Theory, Academic Press, New York.

    Google Scholar 

  10. D. Ji (1987), Nonlinear Filtering with Small Observation Noise, Ph.D. Thesis, Brown University.

  11. R. E. Kalman and R. S. Bucy (1961), New results in linear filtering and prediction theory, J. Basic Engrg ASME, 83:95–108.

    Google Scholar 

  12. R. Katzur, B. Z. Bobrovsky, and Z. Schuss (1984), Asymptotic analysis of the optimal filtering problem for one-dimensional diffusions measured in a low noise channel, I, II, SIAM J. Appl. Math., 44:591–604, and 44:1176–1191.

    Google Scholar 

  13. H. J. Kushner (1964), On the differential equations satisfied by the conditional probability densities of Markov processes, SIAM J. Control, 2:106–119.

    Google Scholar 

  14. F. Le Gland (1981), Estimation de paramètres dans les processus stochastiques, en observation incomplète—Application à un problème de radio-astronomie, Thèse de Docteur-Ingenieur, Université Paris IX-Dauphine.

  15. R. Ch. Liptzer and A. N. Shyriaev (1977), Statistics of Random Processes, Vols. I-II, Springer-Verlag, New York.

    Google Scholar 

  16. P. Milheiro de Oliveira (1990), Etudes asymptotiques en filtrage non linéaire avec petit bruit d'observation, Thèse, Université de Provence.

  17. P. Milheiro de Oliveira and M. C. Roubaud (1991), Filtrage linéaire par morceaux d'un système en temps discret avec petit bruit d'observation, Rapport 1451, INRIA.

  18. D. Ocone and E. Pardoux (1989), A Lie-algebraic criterion for nonexistence of finite-dimensionally computable filters, Proc. 3rd Trente Conf. on SPDEs II, G. Da Prato and L. Tubaro, eds., Lecture Notes in Mathematics, No. 1390, Springer-Verlag, Berlin.

    Google Scholar 

  19. E. Pardoux and M. C. Roubaud (1991), Finite-dimensional approximate filter in case of high signal-to-noise ratio, Stochastic Analysis, E. Merzbach, A. Shwartz, and E. Mayer-Wolf, eds., Academic Press, New York, pp. 433–448.

    Google Scholar 

  20. J. Picard (1986), Nonlinear filtering of one-dimensional diffusions in the case of a high signal-to-noise ratio, SIAM J. Appl. Math., 46:1098–1125.

    Google Scholar 

  21. J. Picard (1987), Asymptotic study of estimation problems with small observation noise, in Stochastic Modelling and Filtering, Lecture Notes in Control and Information Science, Vol. 91, Springer-Verlag, Berlin.

    Google Scholar 

  22. J. Picard (1991), Efficiency of the extended Kalman filter for nonlinear systems with small noise, SIAM J. Appl. Math., 51:843–885.

    Google Scholar 

  23. M. C. Roubaud (1990), Filtrage linéaire par morçeaux avec petit bruit d'observation, Thèse, Université de Provence.

  24. W. M. Wonham (1985), Linear Multivariable Control: a Geometric Approach, 3rd edn. Springer-Verlag, New York.

    Google Scholar 

  25. M. Zakai (1969), On the optimal filtering of diffusion processes, Z. Wahrsch. Verw. Gebiete, 11:230–243.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Roubaud, M.C. Filtrage linéaire par morceaux avec petit bruit d'observation. Appl Math Optim 32, 163–194 (1995). https://doi.org/10.1007/BF01185229

Download citation

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01185229

Mots-clés

AMS classification

Navigation