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On Subspace Based Sinusoidal Frequency Estimation
KTH, Superseded Departments, Signals, Sensors and Systems.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-6855-5868
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
1999 (English)In: International Conference on Acoustics, Speech, and Signal Processing, 1999, Vol. 3, 1565-1568 p.Conference paper (Refereed)
Abstract [en]

Subspace based methods for frequency estimation rely on a low-rank system model that is obtained by collecting the observed scalar valued data samples into vectors. Estimators such as MUSIC and ESPRIT have for some time been applied to this vector model. Also, a statistically attractive Markov-like procedure for this class of methods has been proposed in the literature. Herein, the Markov estimator is re-investigated. Several results regarding rank, performance, and structure are given in a compact manner. The results are used to establish the large sample equivalence of the Markov estimator and the approximate maximum likelihood (AML) algorithm proposed by Stoica et al. (see Automatica, vol.30, no.1, p.131-45, 1994).

Place, publisher, year, edition, pages
1999. Vol. 3, 1565-1568 p.
Keyword [en]
Additive noise, Covariance matrix, Data models, Frequency estimation, Maximum likelihood estimation, Multiple signal classification, Parameter estimation, Sensor systems, Signal processing, Signal processing algorithms
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-86440DOI: 10.1109/ICASSP.1999.756285OAI: diva2:500706
ICASSP, Phoenix, AZ, USA, Mar. 1999
NR 20140805Available from: 2012-02-13 Created: 2012-02-13 Last updated: 2012-02-13Bibliographically approved

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Kristensson, MartinJansson, MagnusOttersten, Björn
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