This paper deals with a new and systematic method of approximating exact nonlinear filters with finite dimensional filters. The method used here is based on the differential geometric approach to statistics. The projection filter is derived in the case of exponential families. A characterization of the filters is given in terms of an assumed density principle. An a posteriori measure of the performance of the projection filter is defined. Applications to particular systems, and numerical schemes which can be used to implement the projection filter are given in the final part. The results of simulations for the cubic sensor are discussed.Cet article propose une méthode nouvelle et systématique pour l'approximation d'un filtre non-linéaire ex...
Over the past few decades, the computational power has been increasing rapidly. With advances of the...
This dissertation presents five different solutions to the nonlinear filtering problem. Three filter...
In this paper, we develop a novel method for approximate continuous-discrete Bayesian filtering. The...
This paper deals with a new and systematic method of approximating exact nonlinear filters with fini...
International audienceThis paper presents a new and systematic method of approximating exact nonline...
This paper presents a new and systematic method of approximating exact nonlinear filters with finite...
: We present a new and systematic method of approximating exact nonlinear filters with finite dimens...
International audienceWe present the projection filter, an approximate finite-dimensional filter bas...
This paper compares the classical concept of assumed density filters (ADF) with a new class of appro...
The projection filter is a technique for approximating the solutions of optimal filtering problems. ...
Funding Information: Muhammad Emzir would like to express his gratitude to the KFUPM Dean of Researc...
The conditional probability density function of the state of a stochastic dynamic system represents ...
This paper compares the classical concept of assumed density filters (ADF) with a new class of appro...
Cover title.Includes bibliographical references (p. 16-18).Research supported by the Air Force Offic...
This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of...
Over the past few decades, the computational power has been increasing rapidly. With advances of the...
This dissertation presents five different solutions to the nonlinear filtering problem. Three filter...
In this paper, we develop a novel method for approximate continuous-discrete Bayesian filtering. The...
This paper deals with a new and systematic method of approximating exact nonlinear filters with fini...
International audienceThis paper presents a new and systematic method of approximating exact nonline...
This paper presents a new and systematic method of approximating exact nonlinear filters with finite...
: We present a new and systematic method of approximating exact nonlinear filters with finite dimens...
International audienceWe present the projection filter, an approximate finite-dimensional filter bas...
This paper compares the classical concept of assumed density filters (ADF) with a new class of appro...
The projection filter is a technique for approximating the solutions of optimal filtering problems. ...
Funding Information: Muhammad Emzir would like to express his gratitude to the KFUPM Dean of Researc...
The conditional probability density function of the state of a stochastic dynamic system represents ...
This paper compares the classical concept of assumed density filters (ADF) with a new class of appro...
Cover title.Includes bibliographical references (p. 16-18).Research supported by the Air Force Offic...
This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of...
Over the past few decades, the computational power has been increasing rapidly. With advances of the...
This dissertation presents five different solutions to the nonlinear filtering problem. Three filter...
In this paper, we develop a novel method for approximate continuous-discrete Bayesian filtering. The...