C. Andrieu and A. Doucet, Online expectation-maximization type algorithms for parameter estimation in general state space models, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., pp.69-72, 2003.
DOI : 10.1109/ICASSP.2003.1201620

S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for on-line nonlinear/non-gaussian bayesian tracking, 2001.

C. Berzuini and W. Gilks, Following a moving average Monte Carlo inference for dynamic Bayesian models, Journal of Royal Statistical Society, B, vol.63, pp.127-146, 2001.

C. Carter and R. Kohn, On Gibbs sampling for state space models, Biometrika, vol.81, issue.3, pp.541-553, 1994.
DOI : 10.1093/biomet/81.3.541

G. Celeux, J. Marin, R. , and C. , Iterated importance sampling in missing data problems, Computational Statistics & Data Analysis, vol.50, issue.12, pp.3386-3404, 2006.
DOI : 10.1016/j.csda.2005.07.018
URL : https://hal.archives-ouvertes.fr/inria-00070473

Z. Chen and S. Haykin, On Different Facets of Regularization Theory, Neural Computation, vol.6, issue.12, pp.2791-2846, 2002.
DOI : 10.1007/BF01414873

D. Crisan and A. Doucet, Convergence of sequential monte carlo methods, 2000.

P. M. Djuric, J. Kotecha, F. Esteve, and E. Perret, Sequential Parameter Estimation of Time-Varying Non-Gaussian Autoregressive Processes, EURASIP Journal on Advances in Signal Processing, vol.2002, issue.8, pp.865-875, 2002.
DOI : 10.1155/S1110865702205089

A. Doucet, N. De-freitas, G. , and N. , Sequential Monte Carlo Methods in Practice, 2001.
DOI : 10.1007/978-1-4757-3437-9

A. Doucet, S. Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering, Statistics and Computing, vol.10, issue.3, pp.197-208, 2000.
DOI : 10.1023/A:1008935410038

A. Doucet and C. Tadic, Parameter estimation in general state-space models using particle methods, Annals of the Institute of Statistical Mathematics, vol.94, issue.2, pp.409-422, 2003.
DOI : 10.1007/BF02530508

P. Fearnhead, Markov chain Monte Carlo, Sufficient Statistics, and Particle Filters, Journal of Computational and Graphical Statistics, vol.11, issue.4, pp.848-862, 2002.
DOI : 10.1198/106186002835

N. Gordon, D. Salmond, and A. F. Smith, Novel approach to nonlinear and nongaussian bayesian state estimation, IEE Proceedings-F, vol.140, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

J. Hamilton, Time Series Analysis, 1994.

J. D. Hamilton, A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle, Econometrica, vol.57, issue.2, pp.357-384, 1989.
DOI : 10.2307/1912559

J. Harrison and M. West, Bayesian Forecasting and Dynamic Models, 1989.

A. Harvey, Forecasting, structural time series models and the Kalman filter, 1989.

R. Kalman, A New Approach to Linear Filtering and Prediction Problems, Journal of Basic Engineering, vol.82, issue.1, pp.35-45, 1960.
DOI : 10.1115/1.3662552

R. Kalman and R. Bucy, New Results in Linear Filtering and Prediction Theory, Journal of Basic Engineering, vol.83, issue.1, pp.95-108, 1960.
DOI : 10.1115/1.3658902

G. Kitagawa, A self-organized state-space model, Journal of the American Statistical Association, vol.93, issue.443, pp.1203-1215, 1998.

J. Liu and R. Chen, Sequential Monte Carlo Methods for Dynamic Systems, Journal of the American Statistical Association, vol.24, issue.443, pp.1032-1044, 1998.
DOI : 10.1073/pnas.94.26.14220

J. Liu and M. West, Combined Parameter and State Estimation in Simulation-Based Filtering, Sequential Monte Carlo Methods in Practice, 2001.
DOI : 10.1007/978-1-4757-3437-9_10

P. Maybeck, Stochastic Models, Estimation, and Control, Volume I, IEEE Transactions on Systems, Man, and Cybernetics, vol.10, issue.5, 1982.
DOI : 10.1109/TSMC.1980.4308494

C. Musso, N. Oudjane, L. , and F. , Improving Regularised Particle Filters, Sequential Monte Carlo Methods in Practice, 2001.
DOI : 10.1007/978-1-4757-3437-9_12

N. Oudjane, Stabilité et approximation particulaires en filtrage non-linéaire. Application au pistage, Thèse du Doctorat en Science, 2000.

M. Pitt and N. Shephard, Filtering via Simulation: Auxiliary Particle Filters, Journal of the American Statistical Association, vol.24, issue.446, pp.94590-599, 1999.
DOI : 10.1016/0005-1098(71)90097-5

N. G. Polson, J. R. Stroud, and P. Müller, Practical filtering with sequential parameter learning, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.140, issue.2, 2002.
DOI : 10.1016/j.jspi.2005.12.005
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.1965

V. Rossi, Filtrage non linéaire par noyaux de convolution ApplicationàApplicationà un procédé de dépollution biologique, Thèse du Doctorat en Science, 2004.

N. Shephard and M. Pitt, Likelihood analysis of non-Gaussian measurement time series, Biometrika, vol.84, issue.3, pp.653-667, 1997.
DOI : 10.1093/biomet/84.3.653

G. Storvik, Particle filters for state-space models with the presence of unknown static parameters, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.281-289, 2002.
DOI : 10.1109/78.978383

M. West, Mixture models, Monte Carlo, Bayesian updating and dynamic models, Computer Science and Statistics, vol.24, pp.325-333, 1992.

M. West, Approximating posterior distribution by mixtures, Journal of Royal Statistical Society, B, vol.55, pp.409-442, 1993.

I. Unité-de-recherche, . Lorraine, . Loria, and . Technopôle-de-nancy, Brabois -Campus scientifique 615, rue du Jardin Botanique -BP 101 -54602 Villers-lès-Nancy Cedex (France) Unité de recherche INRIA Rennes : IRISA, Campus universitaire de Beaulieu -35042 Rennes Cedex (France) Unité de recherche INRIA Rhône-Alpes : 655, avenue de l'Europe -38334 Montbonnot Saint-Ismier (France) Unité de recherche INRIA Rocquencourt, Domaine de Voluceau -Rocquencourt -BP 105 -78153 Le Chesnay Cedex (France) Unité de recherche INRIA Sophia Antipolis : 2004, route des Lucioles -BP 93 -06902 Sophia Antipolis Cedex