C. Åkerblom-]-Åkerblom, Tracking Mobile Phones in Urban Areas, 2000.

M. Anderson, B. D. Anderson, and J. B. Moore, Optimal Filtering, IEEE Transactions on Systems, Man, and Cybernetics, vol.12, issue.2, 1978.
DOI : 10.1109/TSMC.1982.4308806

. Arulampalam, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, Special issue on Monte Carlo Methods for Statistical Signal Processing )), pp.174-188, 2002.
DOI : 10.1109/78.978374

. Balac, . Mazet, S. Balac, and O. Mazet, Introduction aux probabilités, 2005.

D. Bartoli, . Moral, N. Bartoli, D. Moral, and P. , Simulation et Algorithmes Stochastiques, 2001.

L. E. Baum, An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes, Inequalities III, pp.1-8, 1969.

E. Baum, L. E. Baum, and J. Eagon, An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology, Bulletin of the American Mathematical Society, vol.73, issue.3, pp.360-363, 1967.
DOI : 10.1090/S0002-9904-1967-11751-8

P. Baum, L. E. Baum, and T. Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, vol.37, issue.6, pp.1554-1563, 1966.
DOI : 10.1214/aoms/1177699147

S. Baum, L. E. Baum, and G. R. Sell, Growth transformations for functions on manifolds, Pacific Journal of Mathematics, vol.27, issue.2, pp.211-227, 1968.
DOI : 10.2140/pjm.1968.27.211

N. Bouleau, Probabilités de l'ingénieur : variables aléatoires et simulation, 1986.

. Box, . Muller, G. Box, and M. Muller, A Note on the Generation of Random Normal Deviates, The Annals of Mathematical Statistics, vol.29, issue.2, pp.610-611, 1958.
DOI : 10.1214/aoms/1177706645

. Burgers, Analysis Scheme in the Ensemble Kalman Filter, Monthly Weather Review, vol.126, issue.6, pp.1719-1724, 1998.
DOI : 10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2

K. Dahia, Nouvelles méthodes en filtrage particulaire ? Application au recalage de navigation inertielle par mesures altimétriques, Thèse, LMC/IMAG, 2005.

B. Delyon, Simulation et modélisation, Université Rennes, vol.1, 2006.

. Dempster, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, B, vol.39, pp.1-38, 1977.

. Doucet, Sequential Monte Carlo Methods in Practice, Statistics for Engineering and Information Science, 2001.
DOI : 10.1007/978-1-4757-3437-9

. Durbin, Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids, 1998.
DOI : 10.1017/CBO9780511790492

. Elliott, Hidden Markov Models : Estimation and Control, Applications of Mathematics, vol.29, 1995.

G. Evensen, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, Journal of Geophysical Research, vol.109, issue.Part 4, pp.10143-10162, 1994.
DOI : 10.1029/94JC00572

G. Evensen, The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynamics, vol.53, issue.4, pp.343-367, 2004.
DOI : 10.1007/s10236-003-0036-9

O. Fançois, Note de cours de probabilités, 2004.

. Gordon, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F Radar and Signal Processing, vol.140, issue.2, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

. Gunnarsson, Particle filters for positioning in wireless networks, Proc. of EUSIPCO, 2002.

. Gustafsson, Particle filters for positioning, navigation, and tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, 2002.
DOI : 10.1109/78.978396

P. Guttorp, Stochastic modeling of scientific data, 1995.
DOI : 10.1007/978-1-4899-4449-8

B. Isard, M. Isard, and A. Blake, Condensation ? Conditional density propagation for visual tracking, International Journal of Computer Vision, vol.29, issue.1, pp.5-28, 1998.
DOI : 10.1023/A:1008078328650

J. Jacod, Probabilités ? Cours de Licence. Universités Paris VI et VII, 1999.

A. H. Jazwinskii, Stochastic processes and filtering theory, 1970.

. Julier, . Uhlmann, S. Julier, and J. Uhlmann, A general method for approximating nonlinear transformations of probability distributions, 1996.

. Julier, . Uhlmann, S. J. Julier, and J. K. Uhlmann, <title>Consistent debiased method for converting between polar and Cartesian coordinate systems</title>, Acquisition, Tracking, and Pointing XI, 1997.
DOI : 10.1117/12.277178

U. Julier, S. J. Julier, and J. K. Uhlmann, Unscented Filtering and Nonlinear Estimation, Proceedings of the IEEE, vol.92, issue.3, pp.401-422, 2004.
DOI : 10.1109/JPROC.2003.823141

. Julier, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Transactions on Automatic Control, vol.45, issue.3, pp.477-482, 2000.
DOI : 10.1109/9.847726

R. E. 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

. Kalman, . Bucy, R. E. Kalman, and R. S. Bucy, New Results in Linear Filtering and Prediction Theory, Journal of Basic Engineering, vol.83, issue.1, pp.95-108, 1961.
DOI : 10.1115/1.3658902

. Karlsson, Particle filter and Cramer?Rao lower bound for underwater navigation, Proceedings of ICASSP, Hongkong, 2003.

C. Liu, J. S. Liu, and R. Chen, Sequential Monte Carlo Methods for Dynamic Systems, Journal of the American Statistical Association, vol.24, issue.443, pp.931032-1044, 1998.
DOI : 10.1073/pnas.94.26.14220

A. Markov, An example of statistical investigation in the text of " Eugene Onyegin " illustrating coupling of " tests " in chains, Proceedings of the Academy of Science, pp.153-162, 1913.

. Maskell, Efficient particle filtering for multiple target tracking with application to tracking in structured images, Image and Vision Computing, vol.21, issue.10, pp.931-939, 2003.
DOI : 10.1016/S0262-8856(03)00087-8

P. S. Maybeck, Stochastic Models, Estimation and Control, 1979.

A. Millet, Méthodes de Monte Carlo, Université Paris, vol.6, 2006.

C. Musso and N. Oudjane, Recent particle filter applied to terrain navigation, Proceedings of the Third International Conference on Information Fusion, 2000.
DOI : 10.1109/IFIC.2000.859835

D. Pham, Stochastic Methods for Sequential Data Assimilation in Strongly Nonlinear Systems, Monthly Weather Review, vol.129, issue.5, pp.1194-1207, 2001.
DOI : 10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2
URL : https://hal.archives-ouvertes.fr/inria-00073082

A. Poritz, Hidden Markov models: a guided tour, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, pp.7-13, 1988.
DOI : 10.1109/ICASSP.1988.196495

. Pérez, Data Fusion for Visual Tracking With Particles, Proc. IEEE, p.92, 2004.
DOI : 10.1109/JPROC.2003.823147

L. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, vol.77, issue.2, pp.257-286, 1989.
DOI : 10.1109/5.18626

. Rabiner, L. R. Juang-]-rabiner, and B. Juang, Fundamentals of Speech Recognition, 1993.

. Ristic, Beyond the Kalman Filter : Particle Filters for Tracking Applications, 2004.

C. Robert, L'Analyse Statistique Bayesienne, 1992.

. Salmond, . Birch, D. Salmond, and H. Birch, A particle filter for track-before-detect, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), pp.3755-3760, 2001.
DOI : 10.1109/ACC.2001.946220

S. Thrun, Particle filters in robotics, Proceedings of the 17th Annual Conference on Uncertainty in AI (UAI), 2002.