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 goal of this study is to compare the performances of the ensemble Kalman filter and a reduced-rank extended Kalman filter when applied to different dynamic regimes. Data assimilation experiments are performed using an eddy-resolving quasi-geostrophic model of the winddriven ocean circulation. By changing eddy viscosity, this model exhibits two qualitatively distinct behaviors: strongly chao...

The main aim of the Speech Enhancement algorithms is to improve the Quality of speech. The Quality of speech is expressed in two parameters. One is clarity, and another is intelligibility. In this paper, we proposed a method to improve the quality of speech based on computationally efficient AR modeled Iterative Kalman Filter with time and frequency mask. This approach is based on reconstructio...

The Kalman filter has been used successfully in different prediction applications or state determination of a system. One of the more important fields in computer vision is the object tracking. Different movement conditions and occlusions can hinder the vision tracking of an object. In this report we present the use of the Kalman filter as a predictor in vision tracking. We consider the capacit...

In this paper, a novel method for single-channel speech enhancement based on Kalman filtering is proposed. Instead of applying the Kalman algorithm for full-band speech or uniform sub-band speech, speech enhancement is performed by applying the Kalman algorithm to non-uniform sub-band signals obtained from the decomposition of whole-band speech using gammatone filters. Simulation results indica...

This paper describes the application of an unscented Kalman filter to a coal runof-mine bin. A dynamic model of the bin is derived using the principle of mass conservation. The dynamic model is nonlinear with unknown parameters that are identified using actual plant production data. The identified dynamic model is used by an unscented Kalman filter to update the states of the system to improve ...

In this paper, a new filter in the nonlinear Kalman filtering framework is proposed. The referred to as lattice (LKF) and based on class of quasi-Monte Carlo (QMC) methods known rules. proposed LKF method uses Korobov type rule deterministically generate sample points that are randomly shifted Cranley-Patterson shift order approximate multi-dimensional integrals Gaussian context. mathematical f...

An adaptive autoregressive moving average (ARMA) modelling of nonstationary EEG by means of Kalman smoother is presented. The main advantage of the Kalman smoother approach compared to other adaptive algorithms such as LMS or RLS is that the tracking lag can be avoided. This advantage is clearly presented with simulations. Kalman smoother is also applied to tracking of alpha band characteristic...