IterML: A Fast, Robust Algorithm for Estimating Signals With Finite Rate of Innovation

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Sampling signals with finite rate of innovation

Consider classes of signals which have a ̄nite number of degrees of freedom per unit of time, and call this number the rate of innovation of a signal. Examples of signals with ̄nite rate of innovation include stream of Diracs (e.g. the Poisson process), non-uniform splines and piecewise polynomials. Eventhough these signals are not bandlimited, we show that they can be sampled uniformly at (or ab...

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Sampling signals with finite rate of innovation: the noisy case

In [1] a sampling theorem for a certain class of signals with finite rate of innovation (which includes for example stream of Diracs) has been developed. In essence, such non band-limited signals can be sampled at or above the rate of innovation. In the present paper, we consider the case of such signals when noise is present. Clearly, the finite rate of innovation property is lost, but if the ...

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Reconstructing Signals with Finite Rate of Innovation from Noisy Samples

A signal is said to have finite rate of innovation if it has a finite number of degrees of freedom per unit of time. Reconstructing signals with finite rate of innovation from their exact average samples has been studied in SIAM J. Math. Anal., 38(2006), 1389-1422. In this paper, we consider the problem of reconstructing signals with finite rate of innovation from their average samples in the p...

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ژورنال

عنوان ژورنال: IEEE Transactions on Signal Processing

سال: 2013

ISSN: 1053-587X,1941-0476

DOI: 10.1109/tsp.2013.2276411