Spectral analysis of nonuniformly sampled data -- a review
Digital Signal Processing
Wideband spectrum sensing technique based on random sampling on grid: Achieving lower sampling rates
Digital Signal Processing
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A class of spectral estimates of continuous-time stationary stochastic processesX(t)from a finite number of observations{X(t_{n})}^{N}_{n}=ltaken at Poisson sampling instants{t_{n}}is considered. The asymptotic bias and covariance of the estimates are derived, and the influence of the spectral windows and the sampling rate on the performance of the estimates is discussed. The estimates are shown to be consistent under mild smoothness conditions on the spectral density. Comparison is made with a related class of spectral estimates suggested in [15] where the number of observations is {em random}. It is shown that the periodograms of the two classes have distinct statistics.