Reconstruction of Finite Rate of Innovation Signals with Model-Fitting Approach

Zafer Dogan*, Christopher Gilliam, Thierry Blu, Dimitri Van De Ville

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Finite rate of innovation (FRI) is a recent framework for sampling and reconstruction of a large class of parametric signals that are characterized by finite number of innovations (parameters) per unit interval. In the absence of noise, exact recovery of FRI signals has been demonstrated. In the noisy scenario, there exist techniques to deal with non-ideal measurements. Yet, the accuracy and resiliency to noise and model mismatch are still challenging problems for real-world applications. We address the reconstruction of FRI signals, specifically a stream of Diracs, from few signal samples degraded by noise and we propose a new FRI reconstruction method that is based on a model-fitting approach related to the structured-TLS problem. The model-fitting method is based on minimizing the training error, that is, the error between the computed and the recovered moments (i.e., the FRI-samples of the signal), subject to an annihilation system. We present our framework for three different constraints of the annihilation system. Moreover, we propose a model order selection framework to determine the innovation rate of the signal; i.e., the number of Diracs by estimating the noise level through the training error curve. We compare the performance of the model-fitting approach with known FRI reconstruction algorithms and Cramér-Rao's lower bound (CRLB) to validate these contributions.

Original languageEnglish
Article number7169606
Pages (from-to)6024-6036
Number of pages13
JournalIEEE Transactions on Signal Processing
Issue number22
Publication statusPublished - 15 Nov 2015

Bibliographical note

Publisher Copyright:
© 2015 IEEE.


  • Annihilating Filter
  • Cadzow
  • Cramér-Rao's lower bound (CRLB)
  • finite-rate-of-innovation
  • iterative quadratic maximum likelihood (IQML)
  • Kumaresan-Tufts
  • matrix pencil
  • model fitting
  • noise
  • reconstruction
  • sampling
  • structured total least squares (STLS)
  • total least squares (TLS).

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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