Add to ORKGHidden Markov models are used in countless signal processing problems, and the associated nonlinear filtering algorithms are used to obtain posterior distributions for the hidden states. One reason why posterior distributions are so important is because they are used to compute estimates which are optimal given a history of observed data. However, it is often the case that implementation of these algorithms is near impossible because of the curse-of-dimensionality which results from testing every possible hypothesis. This thesis explores new applications of nonlinear filtering and addresses several issues in algorithm implementation
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
This work examines the H∞ filtering issue for Markov jump systems in the circumstances of partial in...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
AbstractFor many nonlinear dynamic systems, the choice of nonlinear Bayesian filtering algorithms is...
The hidden Markov model (HMM) is one of the workhorse tools in, for example, statistical signal proc...
: We present a new and systematic method of approximating exact nonlinear filters with finite dimens...
The problem of discrete universal filtering, in which the components of a discrete signal emitted by...
To my mother and the loving memory of my father Bayesian filtering refers to the process of sequenti...
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed ...
Deposited with permission of the author. © 1998 Dr. Jamie Scott EvansThe focus of this thesis is non...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
International audienceWe propose a new nonlinear Bayesian filtering algorithm where the prediction s...
In this dissertation, we study the implementation of nonlinear filtering algorithms that can be used...
This dissertation presents solutions to two open problems in estimation theory. The first is a tract...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
This work examines the H∞ filtering issue for Markov jump systems in the circumstances of partial in...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
AbstractFor many nonlinear dynamic systems, the choice of nonlinear Bayesian filtering algorithms is...
The hidden Markov model (HMM) is one of the workhorse tools in, for example, statistical signal proc...
: We present a new and systematic method of approximating exact nonlinear filters with finite dimens...
The problem of discrete universal filtering, in which the components of a discrete signal emitted by...
To my mother and the loving memory of my father Bayesian filtering refers to the process of sequenti...
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed ...
Deposited with permission of the author. © 1998 Dr. Jamie Scott EvansThe focus of this thesis is non...
In principle, general approaches to optimal nonlinear filtering can be described in a unified way fr...
International audienceWe propose a new nonlinear Bayesian filtering algorithm where the prediction s...
In this dissertation, we study the implementation of nonlinear filtering algorithms that can be used...
This dissertation presents solutions to two open problems in estimation theory. The first is a tract...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...
This work examines the H∞ filtering issue for Markov jump systems in the circumstances of partial in...
The computational task of continuous-time state estimation, nonlinear filtering and identification, ...