Comparar métodos

	Filtro de Kalman de Conjunto	Filtro de Partículas (Monte Carlo Secuencial)
Campo	Fusión de datos	Bayesiano
Familia	Regression model	Bayesian methods
Año de origen	1994	1993
Autor original	Geir Evensen	Gordon, Salmond & Smith
Tipo	Sequential Monte Carlo data assimilation filter	Sequential Monte Carlo estimator
Fuente seminal	Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162.	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.
Alias	EnKF, Monte Carlo Kalman Filter, Stochastic Ensemble Filter, Topluluk Kalman Filtresi	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Relacionados	3	4
Resumen	The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo data assimilation algorithm introduced by Geir Evensen in 1994. It extends the classical Kalman filter to high-dimensional, nonlinear dynamical systems by representing the forecast error covariance through a finite ensemble of model realizations rather than propagating a full covariance matrix. Each ensemble member evolves through the nonlinear model, and observations are assimilated by computing a sample-based Kalman gain, making the method computationally tractable for large geophysical models.	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.