In principle, general approaches to optimal nonlinear filtering can be described in a unified way from the recursive Bayesian approach. The central idea to this recur- sive Bayesian estimation is to determine the probability density function of the state vector of the nonlinear systems conditioned on the available measurements. However, the optimal exact solution to this Bayesian filtering problem is intractable since it requires an infinite dimensional process. For practical nonlinear filtering applications approximate solutions are required. Recently efficient and accurate approximate non- linear filters as alternatives to the extended Kalman filter are proposed for recursive nonlinear estimation of the states and parameters of dynamical ...
Today, accurate navigation systems often consists of several separate navigation systems which are i...
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Nonlinear estimation and filt...
Recursive Bayesian state estimation is a powerful methodology which is useful for the integration of...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
AbstractFor many nonlinear dynamic systems, the choice of nonlinear Bayesian filtering algorithms is...
This dissertation addresses the problem of parameter and state estimation of nonlinear dynamical sys...
A general approach to optimal nonlinear filtering can be described by a recursive Bayesian approach....
In this dissertation we address nonlinear techniques in filtering,estimation, and detection that ari...
The topic of this thesis is estimation of nonlinear dynamical systems, focusing on the use of method...
This M.Sc. thesis intends to evaluate various algorithms based on Bayesian statistical theory and v...
An optimal estimator for continuous nonlinear systems with nonlinear dynamics, and nonlinear measure...
The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recentl...
The purpose of this dissertation is to develop nonlinear filters and demonstrate their applications....
When dealing with imperfect data and general models of dynamic systems, the best estimate is always ...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
Today, accurate navigation systems often consists of several separate navigation systems which are i...
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Nonlinear estimation and filt...
Recursive Bayesian state estimation is a powerful methodology which is useful for the integration of...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
AbstractFor many nonlinear dynamic systems, the choice of nonlinear Bayesian filtering algorithms is...
This dissertation addresses the problem of parameter and state estimation of nonlinear dynamical sys...
A general approach to optimal nonlinear filtering can be described by a recursive Bayesian approach....
In this dissertation we address nonlinear techniques in filtering,estimation, and detection that ari...
The topic of this thesis is estimation of nonlinear dynamical systems, focusing on the use of method...
This M.Sc. thesis intends to evaluate various algorithms based on Bayesian statistical theory and v...
An optimal estimator for continuous nonlinear systems with nonlinear dynamics, and nonlinear measure...
The focus of this paper is Bayesian state and parameter estimation using nonlinear models. A recentl...
The purpose of this dissertation is to develop nonlinear filters and demonstrate their applications....
When dealing with imperfect data and general models of dynamic systems, the best estimate is always ...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
Today, accurate navigation systems often consists of several separate navigation systems which are i...
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Nonlinear estimation and filt...
Recursive Bayesian state estimation is a powerful methodology which is useful for the integration of...