Comparer des méthodes

	Filtre particulaire robuste	Filtre de Kalman
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1998-2004	1960
Auteur d'origine	Hurzeler & Kunsch; Ristic, Arulampalam & Gordon	Rudolf E. Kalman
Type	Sequential Bayesian estimation	recursive Bayesian filter
Source fondatrice	Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. ISBN: 978-1580536318	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Alias	RPF, robust sequential Monte Carlo, outlier-robust particle filter, heavy-tailed particle filter	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Apparentées	6	5
Résumé	The Robust Particle Filter is a sequential Monte Carlo method that tracks hidden states in nonlinear, non-Gaussian systems while remaining resistant to outliers and model misspecification. It replaces the standard Gaussian likelihood with a heavy-tailed or bounded-influence density, so that anomalous observations receive downweighted importance and cannot derail the state estimate.	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.