Tunable line spectral estimators based on state-covariance subspace analysis

Ali Nasiri Amini, Tryphon T. Georgiou

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Subspace methods for spectral analysis can be adapted to the case where state covariance of a linear filter replaces the traditional Toeplitz matrix formed out of a partial autocorrelation sequence of a time series. This observation forms the basis of a new framework for spectral analysis. The goal of this paper is to quantify potential advantages in working with state-covariance data instead of the autocorrelation sequence. To this end, we identify tradeoffs between resolution and robustness in spectral estimates and how these are affected by the filter dynamics. The approach leads to a novel tunable high-resolution frequency estimator.

Original languageEnglish (US)
Pages (from-to)2662-2671
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number7
StatePublished - Jul 2006

Bibliographical note

Funding Information:
Manuscript received May 21, 2004; revised June 15, 2005. This work was supported in part by NSF and AFSOR.The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Steven L. Grant.


  • Harmonic decomposition
  • Spectral estimation
  • State-covariance
  • Subspace methods


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