In this paper, we describe a new adaptive system for the enhancement of autoregressive (AR) signals which are disturbed by additive broadband noise. The system is comprised of an adaptive Kalman filter operating as a fixed-lag smoother and a subsystem for AR parameter estimation. As opposed to the conventional approach of employing an extended Kalman filter, we estimate the Kalman filter parame...

There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-ga...

Exploring information theory concepts in various filtering applications is not a new idea. It has been done for decades and the number of papers written are beyond the scope of this project. A more specific filter, known as the Kalman filter, has not been extensively explored from an information theoretic perspective. The kalman filter’s most common applications has been for the control of comp...

There is a growing interest in using Kalman-filter models for brain modelling. In turn, it is of considerable importance to represent Kalman-filter in connectionist forms with local Hebbian learning rules. To our best knowledge, Kalman-filter has not been given such local representation. It seems that the main obstacle is the dynamic adaptation of the Kalman-gain. Here, a connectionist represen...

Detection and tracking is the most important technique in the synthetic environment. In this paper we are proposed the real time detection and tracking algorithm using Kalman filter. Kalman filter is based on the assumption base, it is the predictable algorithm through which we can detect and track the human poses. In this paper we design an algorithm to the track of human poses with the help o...

The Kalman filter combines forecasts and new observations to obtain an estimation which is optimal in the sense of a minimum average quadratic error. The Kalman filter has two main restrictions: (i) the dynamical system is assumed linear and (ii) forecasting errors and observational noises are projected onto Gaussian distributions. Here, we offer an important generalization to the case where er...

A solution to the state estimation problem of systems with unmeasurable non-zero mean inputs/disturbances, which do not satisfy the disturbance decoupling conditions, is given using the Kalman filtering and Bayesian estimation theory. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle ...

Visual contour tracking in complex background is a difficult task. The measurement model is often nonlinear due to clutter in images. Traditional visual tracker based on Kalman filter employs simple linear measurement model, and often collapses in tracking process. The paper presents a new contour tracker based on Unscented Kalman filter that is superior to extended Kalman filter both in theory...

This paper presents a core approach to design and develop a real-time based Vehicle Tracking System using Kalman filter. It is used to determine the current location of a target device in terms of UTC time , Data status, latitude, longitude, UTC date, Speed over ground in knots, Magnetic variation ,Mode indicators and Checksum information by utilizing the sms features of GSM technology. An atte...

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stoch...