Decimated Prony's Method for Stable Super-Resolution

Rami Katz, Nuha Diab, Dmitry Batenkov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We study recovery of amplitudes and nodes of a finite impulse train from noisy frequency samples. This problem is known as super-resolution under sparsity constraints and has numerous applications. An especially challenging scenario occurs when the separation between Dirac pulses is smaller than the Nyquist-Shannon-Rayleigh limit. Despite large volumes of research and well-established worst-case recovery bounds, there is currently no known computationally efficient method which achieves these bounds in practice. In this work we combine the well-known Prony's method for exponential fitting with a recently established decimation technique for analyzing the super-resolution problem in the above mentioned regime. We show that our approach attains optimal asymptotic stability in the presence of noise, and has lower computational complexity than the current state of the art methods.

Original languageEnglish
Pages (from-to)1467-1471
Number of pages5
JournalIEEE Signal Processing Letters
StatePublished - 2023


  • Prony's method
  • decimation
  • direction of arrival
  • exponential fitting
  • finite rate of innovation
  • sparse super-resolution
  • sub Nyquist sampling


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