Further Results and Insights on Subspace Based Sinusoidal Frequency Estimation
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
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.
Covariance matrix, Correlation, eigenvalues and eigenfunctions, frequency estimation, maximum likelihood estimation, multi- dimensional signal processing, singular value decomposition, spectral analysis.
IdentifiersURN: urn:nbn:se:kth:diva-58835DOI: 10.1109/78.969505OAI: oai:DiVA.org:kth-58835DiVA: diva2:474237
QC 201408052012-01-092012-01-092015-02-02Bibliographically approved