Optimal representation of non-stationary random fields with finite numbers of samples: A linear MMSE framework
Ozaktas, H. M.
Digital Signal Processing
1602 - 1609
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Özçelikkale, A., & Ozaktas, H. M. (2013). Optimal representation of non-stationary random fields with finite numbers of samples: A linear MMSE framework. Digital Signal Processing, 23(5), 1602-1609.
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/12921
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.