Optimal representation of non-stationary random fields with finite numbers of samples: A linear MMSE framework
Digital Signal Processing: A Review Journal
1602 - 1609
Item Usage Stats
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.
Random field estimation
Digital signal processing
Published Version (Please cite this version)http://dx.doi.org/10.1016/j.dsp.2013.05.001
Showing items related by title, author, creator and subject.
Korkmaz, Sayit (IEEE, 2005)In this paper, we present an algebraic description of the aliasing phenomena evident in the linear sampling process of multidimensional periodic band limited signals. Opposed to the classical Shannon sampling, periodic ...
Sayın, I.; Arıkan, F.; Arıkan, Orhan (IEEE, 2007-06)Spatiotemporal variations in the ionosphere affects the HF and satellite communications and navigation systems. Total Electron Content (TEC) is an important parameter since it can be used to analyze the spatial and temporal ...
Kozat, S. S.; Guan, K. M.; Singer, A. C. (Elsevier, 2013)In this paper, we investigate level-crossing (LC) analog-to-digital converters (ADC)s in a competitive algorithm framework. In particular, we study how the level sets of an LC ADC should be selected in order to track the ...