Optimal representation of non-stationary random fields with finite numbers of samples: A linear MMSE framework

Date
2013
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Source Title
Digital Signal Processing: A Review Journal
Print ISSN
1051-2004
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Publisher
Elsevier
Volume
23
Issue
5
Pages
1602 - 1609
Language
English
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Abstract

In this article we consider the representation of a finite-energy non-stationary random field with a finite number of samples. We pose the problem as an optimal sampling problem where we seek the optimal sampling interval under the mean-square error criterion, for a given number of samples. We investigate the optimum sampling rates and the resulting trade-offs between the number of samples and the representation error. In our numerical experiments, we consider a parametric non-stationary field model, the Gaussian-Schell model, and present sampling schemes for varying noise levels and for sources with varying numbers of degrees of freedom. We discuss the dependence of the optimum sampling interval on the problem parameters. We also study the sensitivity of the error to the chosen sampling interval.

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