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Further Results and Insights on Subspace Based Sinusoidal Frequency Estimation
(Nokia Networks, Kista, Sweden)
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-6855-5868
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
2001 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 49, no 12, 2962-2974 p.Article in journal (Refereed) Published
Abstract [en]

Subspace-based methods for parameter identification have received considerable attention in the literature. Starting with a scalar-valued process, it is well known that subspace-based identification of sinusoidal frequencies is possible if the scalar valued data is windowed to form a low-rank vector-valued process. MUSIC and ESPRIT-like estimators have, for some time, been applied to this vector model. In addition, a statistically attractive Markov-like procedure for this class of methods has been proposed. Herein, the Markov-like procedure is reinvestigated. Several results regarding rank, performance, and structure are given in a compact manner. The large sample equivalence with the approximate maximum likelihood method by Stoica et al. (1988) is also established

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2001. Vol. 49, no 12, 2962-2974 p.
Keyword [en]
Covariance matrix, Correlation, eigenvalues and eigenfunctions, frequency estimation, maximum likelihood estimation, multi- dimensional signal processing, singular value decomposition, spectral analysis.
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-58835DOI: 10.1109/78.969505OAI: diva2:474237

QC 20140805

Available from: 2012-01-09 Created: 2012-01-09 Last updated: 2015-02-02Bibliographically approved

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