Ab initio Functional Decomposition of Kalman Filter: A feasibility Analysis on Constrained Least Square Problem

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