Algorithms for adaptive estimation of dynamic objects under the influence of additive noise
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Abstract
Algorithms for adaptive evaluation of dynamic object control systems under the influence of
additive noise are considered. Algorithms of the Kalman filter are given. An adaptive filtering approach for
nonlinear systems with additive noise is also considered. The developed adaptive estimation algorithm can
calculate the square root of the covariance matrix in a simple way in such a way that positive semi-certainty
is guaranteed, which significantly increases the stability and accuracy of the filter.
Description
The Kalman filter is widely used in numerous
tasks of synthesizing and designing systems for
managing dynamic objects of various functional
purposes [1-9]. The Kalman filter provides an unbiased
estimate with minimal variance about the state of a
discrete linearly varying dynamic system, the input and
output of which are distorted by Gaussian white noise
with an additive character. This approach was extended
to continuous dynamical systems by Kalman and Bucy
with a linear character