Title

Reconstructing Signals With Finite Rate Of Innovation From Noisy Samples

Keywords

Mean squared error; Regularized least squares; Sampling; Signals with finite rate of innovation; Wiener filter

Abstract

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 Sun (SIAM J. Math. Anal. 38, 1389-1422, 2006). In this paper, we consider the problem of reconstructing signals with finite rate of innovation from their average samples in the presence of deterministic and random noise. We develop an adaptive Tikhonov regularization approach to this reconstruction problem. Our simulation results demonstrate that our adaptive approach is robust against noise, is almost consistent in various sampling processes, and is also locally implementable. © 2009 Springer Science+Business Media B.V.

Publication Date

7-1-2009

Publication Title

Acta Applicandae Mathematicae

Volume

107

Issue

1-3

Number of Pages

339-372

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s10440-009-9474-9

Socpus ID

67650869601 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/67650869601

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