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.
2019
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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/758898
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact