Kalman filter is an important algorithm used in control theory. It takes an initial state as input and a series of observations over time and output the hidden state. The advection-diffusion equation is a PDE that characterizes the combination effect of advection and diffusion of a given object in the solvent. Such a problem is within the domain that the Kalman filter can solve. In this report, I will first derive the Kalman filter algorithm, then examine its application to an advection-diffusion equation. I will use different metrics to quantify the numerical performance of the algorithm. The contribution of this report lies in the combination of the Kalman filter algorithm with the advection equation. Also, an ample amount of graphs that ...
International audienceA computational simplification of the Kalman filter (KF) is introduced - the p...
AbstractKalman filtering has become a powerful framework for solving data assimilation problems. Of ...
The Kalman filter is the general solution to the recursive, minimised mean square estimation problem...
Data assimilation is a process where an improved prediction is obtained from a weighted combination ...
This dissertation contributes to two mathematical disciplines applied in the Atmospheric Sciences: c...
Diffusion processes provide a natural way of modelling a variety of physical and economic phenomena...
AbstractThe paper reviews and generalizes recent filtering and smoothing algorithms for observations...
Kalman filtering and multiple model adaptive estimation (MMAE) methods have been applied by research...
This paper presents Advection-Diffusion Analysis Using Kalman Filter FEM. The Advection-Diffusion ph...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
The Kalman filter is a tool designed primarily to estimate the values of the `state¿ of a dynamic sy...
This thesis focuses on generating a continuous estimate of state using a small number of sensors for...
The success of the ensemble Kalman filter has triggered a strong interest in expanding its scope bey...
This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be m...
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Inclu...
International audienceA computational simplification of the Kalman filter (KF) is introduced - the p...
AbstractKalman filtering has become a powerful framework for solving data assimilation problems. Of ...
The Kalman filter is the general solution to the recursive, minimised mean square estimation problem...
Data assimilation is a process where an improved prediction is obtained from a weighted combination ...
This dissertation contributes to two mathematical disciplines applied in the Atmospheric Sciences: c...
Diffusion processes provide a natural way of modelling a variety of physical and economic phenomena...
AbstractThe paper reviews and generalizes recent filtering and smoothing algorithms for observations...
Kalman filtering and multiple model adaptive estimation (MMAE) methods have been applied by research...
This paper presents Advection-Diffusion Analysis Using Kalman Filter FEM. The Advection-Diffusion ph...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
The Kalman filter is a tool designed primarily to estimate the values of the `state¿ of a dynamic sy...
This thesis focuses on generating a continuous estimate of state using a small number of sensors for...
The success of the ensemble Kalman filter has triggered a strong interest in expanding its scope bey...
This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be m...
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Inclu...
International audienceA computational simplification of the Kalman filter (KF) is introduced - the p...
AbstractKalman filtering has become a powerful framework for solving data assimilation problems. Of ...
The Kalman filter is the general solution to the recursive, minimised mean square estimation problem...