Reconstructing Signals with Finite Rate of Innovation from Noisy Samples
Abbreviated Journal Title
Acta Appl. Math.
Sampling; Signals with finite rate of innovation; Regularized least; squares; Mean squared error; Wiener filter; SHIFT-INVARIANT SPACES; SHANNON; REGULARIZATION; Mathematics, Applied
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.
Acta Applicandae Mathematicae
Article; Proceedings Paper
"Reconstructing Signals with Finite Rate of Innovation from Noisy Samples" (2009). Faculty Bibliography 2000s. 1355.