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

As 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 have been approached previously, satisfactory solutions are only available for certain classes of sampling kernels, for example, kernels that satisfy the Strang

• Type: Journal
• Year: 2008
• Where: CORR
• Authors: Vincent Yan Fu Tan, Vivek K. Goyal
• Keywords: CORR 2008, Education, Finite Number, Markov Chain Monte Carlo, Strang­Fix Condition