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:(2020), pp. 75-92. (Intervento presentato al convegno 13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019 tenutosi a Bialystok, Poland nel 8 September 2019 - 11 September 2019) [10.1007/978-3-030-43222-5_7].
Ab initio Functional Decomposition of Kalman Filter: A feasibility Analysis on Constrained Least Square Problem
L. D'Amore
;CACCIAPUOTI, ROSALBA;V. Mele
2020
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.
