The standard formulation of Kalman Filter (KF) becomes computationally intractable for solving large scale state space estimation problems as in ocean/weather forecasting due to matrix storage and inversion requirements. We introduce a numerical formulation of KF using Domain Decomposition approach partitioning ab-initio the whole KF computational method. We present its feasibility analysis using the constrained least square model underlying variational data assimilation problems.
Ab initio Functional Decomposition of Kalman Filter: A feasibility Analysis on Constrained Least Square Problem / D'Amore, L.; Cacciapuoti, Rosalba; Mele, V.. - 12044:(2019), pp. 75-92.
Ab initio Functional Decomposition of Kalman Filter: A feasibility Analysis on Constrained Least Square Problem
L. D'Amore
;CACCIAPUOTI, ROSALBA;V. Mele
2019
Abstract
The standard formulation of Kalman Filter (KF) becomes computationally intractable for solving large scale state space estimation problems as in ocean/weather forecasting due to matrix storage and inversion requirements. We introduce a numerical formulation of KF using Domain Decomposition approach partitioning ab-initio the whole KF computational method. We present its feasibility analysis using the constrained least square model underlying variational data assimilation problems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.