Uporedite metode

	Fuzija senzora	Ensemblovani Kalmanov filter
Oblast	Fuzija podataka	Fuzija podataka
Porodica	Process / pipeline	Regression model
Godina nastanka	2013	1994
Tvorac	Khaleghi, Khamis, Karray & Razavi	Geir Evensen
Tip	Multi-source information integration pipeline	Sequential Monte Carlo data assimilation filter
Temeljni izvor	Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44.	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.
Drugi nazivi	Multisensor Data Fusion, Multi-Sensor Integration, Information Fusion, Sensör Füzyonu	EnKF, Monte Carlo Kalman Filter, Stochastic Ensemble Filter, Topluluk Kalman Filtresi
Srodne	3	3
Sažetak	Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art review in Information Fusion, the discipline addresses imperfections such as noise, incompleteness, temporal misalignment, and conflicting readings that arise whenever multiple sensing modalities operate in parallel.	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.