Comparer des méthodes

	Filtre particulaire (Monte Carlo séquentiel)	Filtre de Kalman
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1993	1960
Auteur d'origine	Gordon, Salmond & Smith	Rudolf E. Kalman
Type	Sequential Monte Carlo estimator	recursive Bayesian filter
Source fondatrice	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113.	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Alias	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Apparentées	4	5
Résumé	The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.	The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.