in this paper, we present an application of the stochastic calculusto the problem of modeling electrical networks. the filtering problem have animportant role in the theory of stochastic differential equations(sdes). in thisarticle, we present an application of the continuous kalman-bucy filter for a rlcircuit. the deterministic model of the circuit is replaced by a stochastic model byadding a ...

A method for improving the energy efficiency of OFDM systems based on differential transformation and extrapolation is considered. The possible structure of the extrapolator is analyzed, the implementation of an extrapolator based on the Kalman Bucy filter and the Wiener filter is considered. The results of carried out simulation confirming the effectiveness of the proposed scheme are given.

Abstract We provide a rigorous derivation of the ensemble Kalman–Bucy filter as well transform in case nonlinear, unbounded model and observation operators. identify them continuous time limit discrete-time Kalman square root filters, respectively, together with concrete convergence rates terms discretisation step size. Simultaneously, we establish well-posedness accuracy both continuous-time f...

This paper deals with the filtering problem, an important part of the theory of stochastic differential equations. We present an application of the continuous Kalman-Bucy filter to a special problem of RL electrical circuit.

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,...

The purpose of this paper is to present an alternate derivation of optimal linear filters. The basic technique is the use of a matrix version of the maximum principle of Pontryagin coupled with the use of gradient matrices to derive the optimal values of the ; '1 filter coefficients for minimum variance estimation under the require­ :11" ment that the estimates be unbiased. The optimal filter w...

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...

We consider a linear Gaussian noise channel used with delayed feedback. The channel noise is assumed to be an ARMA (autoregressive and/or moving average) process. We reformulate the Gaussian noise channel into an intersymbol interference channel with white noise, and show that the delayed-feedback of the original channel is equivalent to the instantaneous-feedback of the derived channel. By gen...

This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are pres...

This paper proposes a sampling based kinodynamic planning technique for planning persistent monitoring trajectories for a sensing robot in a spatiotemporal environmental field. The robot uses a Kalman-Bucy filter to estimate the spatiotemporal field. Since the error covariance matrix of the Kalman-Bucy filter evolves according to the nonlinear Riccati differential equation, this requires planni...