NDLI: Sampling of multidimensional Gaussian processes

Content Provider	IEEE Xplore Digital Library
Author	Kazakov, V.A.
Copyright Year	2004
Description	Author affiliation: Dept. of Telecommun., Nat. Polytech. Inst. of Mexico, Mexico City, Mexico (Kazakov, V.A.)
Abstract	The case of the Sampling-Reconstruction Procedure (SRP) of stochastic process with limited number of samples is the most important variant both for theory and practice. In order to determine the optimal reconstruction function and to calculate the minimum error reconstruction function, it is necessary to apply the classical conditional mean rule. In this paper we deal with the statistical description of the SRP of multidimensional Gaussian process when the number of samples is limited. In this case we have sampling of some components of a vector Gaussian process. One can investigate the SRP of all components on the base of all sets of samples. There are two types of linear systems considerations: the first is the series system and the second is the parallel system. The output process of every filter is one component of the vector Gaussian process. The case of the series system is concerned with the linear transformations Z(t) of a given stochastic process y(t). One can see that the reconstruction algorithm based on the multidimensional presentation of the SRP provides the best results. In the same manner one can investigate the case of other linear transformations: the differentiation and the delay.
Starting Page	256
Ending Page	258
File Size	236270
Page Count	3
File Format	PDF
ISBN	0780384113
DOI	10.1109/MSMW.2004.1345838
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-21
Publisher Place	Ukraine
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sampling methods Multidimensional systems Gaussian processes Stochastic processes Vectors Covariance matrix Nonlinear filters Estimation error Probability density function Productivity
Content Type	Text
Resource Type	Article

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