Abstract-This paper proposes a novel method to minimize the risk sensitive cost function based on cubature quadrature algorithm. The proposed filter is named as risk sensitive cubature quadrature Kalman filter (RSCQKF). The theory and formulation of the RSCQKF have been presented in this paper. The performance of proposed risk sensitive filter is compared with its risk neutral counterpart for a ballistic target tracking problem. The simulation results show that for wrongly modeled process noise parameters, the RSCQKF outperforms the cubature quadrature Kalman filter (CQKF)
In this paper, we address the risk-sensitive filtering problem which is minimizing the expectation ...
The purpose of this dissertation is to develop nonlinear filters and demonstrate their applications....
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
In a recent paper [1], we derived a new discrete-time Bayesian filter, which we have named the cubat...
a b s t r a c t The cubature Kalman filter (CKF) is a relatively new addition to derivative-free app...
This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear s...
This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear s...
Abstract—In this paper, we present a new nonlinear filter for high-dimensional state estimation, whi...
In this study, we put forward the robust fractional gain based interpolatory cubature Kalman filter ...
Abstract: In this paper, we present an improved particle filter algorithm for ballistic target track...
Abstract—To resolve the tracking problem of nonlinear/non-Gaussian systems effectively, this paper p...
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems...
The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and s...
Abstract-Tracking a ballistic re-entry target from radar observations is a highly complex problem in...
AbstractThe paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic sys...
In this paper, we address the risk-sensitive filtering problem which is minimizing the expectation ...
The purpose of this dissertation is to develop nonlinear filters and demonstrate their applications....
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
In a recent paper [1], we derived a new discrete-time Bayesian filter, which we have named the cubat...
a b s t r a c t The cubature Kalman filter (CKF) is a relatively new addition to derivative-free app...
This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear s...
This paper presents a new adaptive random weighting cubature Kalman filtering method for nonlinear s...
Abstract—In this paper, we present a new nonlinear filter for high-dimensional state estimation, whi...
In this study, we put forward the robust fractional gain based interpolatory cubature Kalman filter ...
Abstract: In this paper, we present an improved particle filter algorithm for ballistic target track...
Abstract—To resolve the tracking problem of nonlinear/non-Gaussian systems effectively, this paper p...
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems...
The standard Kalman filter is a powerful and widely used tool to perform prediction, filtering and s...
Abstract-Tracking a ballistic re-entry target from radar observations is a highly complex problem in...
AbstractThe paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic sys...
In this paper, we address the risk-sensitive filtering problem which is minimizing the expectation ...
The purpose of this dissertation is to develop nonlinear filters and demonstrate their applications....
This thesis is on filtering in state space models. First, we examine approximate Kalman filters for ...
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