Add to ORKGThe 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
Abstract—Among existing ocean data assimilation method-ologies, reduced-state Kalman filters are a w...
International audienceThe computation of derivatives and the development of tangent and adjoint code...
International audienceThe main purpose of this chapter is to review the fundamentals of the Kalman F...
The standard formulation of Kalman Filter (KF) becomes computationally intractable for solving large...
Kalman filter (KF) is one of the most important and common estimation algorithms. We introduce an in...
The standard formulations of the Kalman filter (KF) and extended Kalman filter (EKF) require storing...
The Kalman filter is a technique for estimating a time-varying state given a dynamical model for, an...
The Kalman filter and its extensions are used in a vast number of aerospace and navigation applicati...
Data assimilation techniques are the state-of-the-art approaches in the reconstruction of a spatio-t...
1. introduction and motivation ThC fu]l nonlinear Kalman filter (KI;) sequential algorithm is, ill t...
The Kalman filter (KF) dates back to 1960, when R. E. Kalman [4] provided a recursive algorithm to c...
this paper is to formulate and evaluate three approximations capable of handling non--normal, unstab...
International audienceThis paper introduces a new approximate solution of the optimal nonlinear filt...
Data Assimilation is a technique for a synthesis of infor-mation from a dynamic (numerical) model an...
Data assimilation is a methodology which can optimise the extraction of reliable information from ob...
Abstract—Among existing ocean data assimilation method-ologies, reduced-state Kalman filters are a w...
International audienceThe computation of derivatives and the development of tangent and adjoint code...
International audienceThe main purpose of this chapter is to review the fundamentals of the Kalman F...
The standard formulation of Kalman Filter (KF) becomes computationally intractable for solving large...
Kalman filter (KF) is one of the most important and common estimation algorithms. We introduce an in...
The standard formulations of the Kalman filter (KF) and extended Kalman filter (EKF) require storing...
The Kalman filter is a technique for estimating a time-varying state given a dynamical model for, an...
The Kalman filter and its extensions are used in a vast number of aerospace and navigation applicati...
Data assimilation techniques are the state-of-the-art approaches in the reconstruction of a spatio-t...
1. introduction and motivation ThC fu]l nonlinear Kalman filter (KI;) sequential algorithm is, ill t...
The Kalman filter (KF) dates back to 1960, when R. E. Kalman [4] provided a recursive algorithm to c...
this paper is to formulate and evaluate three approximations capable of handling non--normal, unstab...
International audienceThis paper introduces a new approximate solution of the optimal nonlinear filt...
Data Assimilation is a technique for a synthesis of infor-mation from a dynamic (numerical) model an...
Data assimilation is a methodology which can optimise the extraction of reliable information from ob...
Abstract—Among existing ocean data assimilation method-ologies, reduced-state Kalman filters are a w...
International audienceThe computation of derivatives and the development of tangent and adjoint code...
International audienceThe main purpose of this chapter is to review the fundamentals of the Kalman F...