The Kalman Bucy filter is a well-known observer to estimate the state vector from the incomplete state measurements. However, when the time delay is taken into account, the filter can become ineffective. In this paper, the identification algorithm presented in a previous paper (Anh 2000) is used to improve the Kalman Bucy filter in the presence of time delay. The differential equation of the ob...

State estimation algorithm deals with recovering some desired state variables of a dynamic system from available noisy measurements, and estimation of the state variables is one of the fundamental and significant problems in control and signal processing areas, and many significant progresses have been made in this area. In 1940s, Wiener, the founder of the modern statistical estimation theory,...

In this paper we describe a novel approach, called improbability filtering, to rejecting false-positive observations from degrading the tracking performance of an Extended Kalman-Bucy fdter. False-positives, incorrect observations reported with a high confidence, are a form of non-Gaussian white noise and therefore degrade the tracking performance of an Extended Kalman-Bucy Filter. Improbabilit...

Mrstract—The notion is exploded that to build a Kalman–Bucy filter, one needs to know tbe whole structure of the signal generating process. It is shown that the filter is constructible knowing precisely those covariwrces required to construct a Wiener filter, and no more, and that the filter is independent of the particular models of the processes generating these covariances. Performance of th...

In this paper, the Kalman-Bucy filter is designed for an Ito-Volterra process over Ito-Volterra observations that cannot be reduced to the case of a differential observation equation. The Kalman-Bucy filter is then designed for an Ito-Volterra process over discontinuous Ito-Volterra observations. Based on the obtained results, the filtering problem over discrete observations with delays is solv...

in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...

The notion is exploded that to build a Kalman-Bucy filter, one needs to know the whole structure of the signal generating process. It is shown that the filter is constructible knowing precisely those covariance~ required to construct a Wiener filter, and no more, and that the filter is independent of the particular models of the processes generating these covariances. Performance of the Kalman-...

This paper is concerned with the filtering problem in continuous-time. Three algorithmic solution approaches for this problem are reviewed: (i) the classical Kalman-Bucy filter which provides an exact solution for the linear Gaussian problem, (ii) the ensemble Kalman-Bucy filter (EnKBF) which is an approximate filter and represents an extension of the Kalman-Bucy filter to nonlinear problems, a...