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	Fuzja danych	Filtr Kalmana z zespołem (Ensemble Kalman Filter)
Dziedzina	Fuzja danych	Fuzja danych
Rodzina	Process / pipeline	Regression model
Rok powstania	1997	1994
Twórca	David Hall & James Llinas	Geir Evensen
Typ	Multi-level information integration pipeline	Sequential Monte Carlo data assimilation filter
Źródło pierwotne	Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23.	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.
Inne nazwy	Sensor Data Fusion, Information Fusion, Multi-source Data Fusion, Veri Füzyonu	EnKF, Monte Carlo Kalman Filter, Stochastic Ensemble Filter, Topluluk Kalman Filtresi
Pokrewne	3	3
Podsumowanie	Data fusion is a multi-level process that combines data and information from multiple sensors and sources to achieve improved accuracy, completeness, and confidence in estimates that cannot be obtained from any single source alone. Formally introduced as the Joint Directors of Laboratories (JDL) model by Hall and Llinas in 1997, the framework organizes fusion into hierarchical processing levels ranging from raw signal combination to higher-order situation and threat assessment.	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.