A maximum entropy enhancement for a family of high-resolution spectral estimators

Augusto Ferrante, Michele Pavon, Mattia Zorzi

Research output: Contribution to journalArticlepeer-review


Structured covariances occurring in spectral analysis, filtering and identification need to be estimated from a finite observation record. The corresponding sample covariance usually fails to possess the required structure. This is the case, for instance, in the Byrnes-Georgiou-Lindquist THREE-like tunable, high-resolution spectral estimators. There, the output covariance Σ of a linear filter is needed to initialize the spectral estimation technique. The sample covariance estimate Σ, however, is usually not compatible with the filter. In this paper, we present a new, systematic way to overcome this difficulty. The new estimate Σ is obtained by solving an ancillary problem with an entropic-type criterion. Extensive scalar and multivariate simulation shows that this new approach consistently leads to a significant improvement of the spectral estimators performances.

Original languageEnglish (US)
Article number5953479
Pages (from-to)318-329
Number of pages12
JournalIEEE Transactions on Automatic Control
Issue number2
StatePublished - Feb 2012


  • Convex optimization
  • covariance extension
  • maximum entropy
  • multivariable spectral estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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