Estimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm

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dc.creatorTan, Vincent Y. F.
dc.creatorGoyal, Vivek K.
dc.date2008-01-01
dc.date2008-01-03
dc.date.accessioned2026-07-07T12:49:42Z
dc.date.available2026-07-07T12:49:42Z
dc.descriptionAs an example of the recently-introduced concept of rate of innovation, signals that are linear combinations of a finite number of Diracs per unit time can be acquired by linear filtering followed by uniform sampling. However, in reality, samples are rarely noiseless. In this paper, we introduce a novel stochastic algorithm to reconstruct a signal with finite rate of innovation from its noisy samples. Even though variants of this problem has been approached previously, satisfactory solutions are only available for certain classes of sampling kernels, for example kernels which satisfy the Strang-Fix condition. In this paper, we consider the infinite-support Gaussian kernel, which does not satisfy the Strang-Fix condition. Other classes of kernels can be employed. Our algorithm is based on Gibbs sampling, a Markov chain Monte Carlo (MCMC) method. Extensive numerical simulations demonstrate the accuracy and robustness of our algorithm.
dc.descriptionSubmitted to IEEE Transactions on Signal Processing
dc.identifierhttps://arxiv.org/abs/0801.0275
dc.identifierhttp://arxiv.org/abs/0801.0275
dc.identifierIEEE Trans. on Signal Processing, vol. 56, no. 10, pp. 5135-5146, October 2008
dc.identifierdoi:10.1109/TSP.2008.928510
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222462
dc.subjectApplications
dc.subjectInformation Theory
dc.titleEstimating Signals with Finite Rate of Innovation from Noisy Samples: A Stochastic Algorithm
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