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- IEEE Transactions on Signal Processing , 2005
"... Recently, it was shown that it is possible to develop exact sampling schemes for a large class of parametric nonbandlimited signals, namely, certain signals of finite rate of innovation . A common feature of such signals is that they have a finite number of degrees of freedom per unit of time an ..."
Abstract - Cited by 20 (1 self) - Add to MetaCart
Recently, it was shown that it is possible to develop exact sampling schemes for a large class of parametric nonbandlimited signals, namely, certain signals of finite rate of innovation . A common feature of such signals is that they have a finite number of degrees of freedom per unit of time and can be reconstructed from a finite number of uniform samples. In order to prove sampling theorems, Vetterli et al. considered the case of deterministic, noiseless signals, and developed algebraic methods that lead to perfect reconstruction. However, when noise is present, many of those schemes can become ill-conditioned. In this paper, we revisit the problem of sampling and reconstruction of signals with finite rate of innovation and propose improved, more robust methods that have better numerical conditioning in the presence of noise and yield more accurate reconstruction. We analyze in detail a signal made up of a stream of Diracs and develop algorithmic tools that will be used as a basis in all constructions. While some of the techniques have been already encountered in the spectral estimation framework, we further explore preconditioning methods that lead to improved resolution performance in the case when the signal contains closely spaced components. For classes of periodic signals, such as piecewise polynomials and nonuniform splines, we propose novel algebraic approaches that solve the sampling problem in the Laplace domain, after appropriate windowing. Building on the results for periodic signals, we extend our analysis to finite-length signals and develop schemes based on a Gaussian kernel, which avoid the problem of ill-conditioning by proper weighting of the data matrix. Our methods use structured linear systems and robust algorithmic solutions, which we show through simulation results.