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
Copyright Status
Unknown
Socpus ID
67650869601 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/67650869601
STARS Citation
Bi, Ning; Nashed, M. Zuhair; and Sun, Qiyu, "Reconstructing Signals With Finite Rate Of Innovation From Noisy Samples" (2009). Scopus Export 2000s. 12132.
https://stars.library.ucf.edu/scopus2000/12132