Finite dimensional FRI

Abstract

Traditional Finite Rate of Innovation (FRI) theory has considered the problem of sampling continuous-time signals. This framework can be naturally extended to the case where the input is a discrete-time signal. Here we present a novel approach which uses both the traditional FRI sampling scheme, based on the annihilating filter method, and the fact that in this new setup the null space of the problem to be solved is finite dimensional. In the noiseless scenario, we show that this new approach is able to perfectly recover the original signal at the critical sampling rate. We also present simulation results in the noisy scenario where this new approach improves performances in terms of the mean squared error (MSE) of the reconstructed signal when compared to the canonical FRI algorithms and compressed sensing (CS).