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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimir: 1064-2315
ISSN En Línea: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v43.i4.30
pages 20-34

Diffuse Initialization of Kalman Filter

Boris A. Skorohod
Sevastopol National Technical University, Ukraine

SINOPSIS

The behavior of Kalman filter is studied at interpretation of unknown initial conditions as the random variables having a covariance matrix proportional to large positive parameter. The developed approach allows one to express characteristics of the filter in an analytic form, to explain a phenomenon of divergence and propose a limiting estimation algorithm which is independent of large initial parameter leading to divergence. As the application there were considered two problems: filtering with a sliding window and a parameter estimation of separable regression. The received results are illustrated by example of training a radial basic neural network.


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