A globally convergent matricial algorithm for multivariate spectral estimation

Federico Ramponi, Augusto Ferrante, Michele Pavon

Research output: Contribution to journalArticlepeer-review


In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as MATLAB's PEM and MATLAB's N4SID.

Original languageEnglish (US)
Pages (from-to)2376-2388
Number of pages13
JournalIEEE Transactions on Automatic Control
Issue number10
StatePublished - 2009


  • Convex optimization
  • Global convergence
  • Hellinger distance
  • Matricial Newton algorithm
  • Multivariable spectrum approximation
  • Spectral estimation

ASJC Scopus subject areas

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


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