On subspace based sinusoidal frequency estimation

Author

Kristensson, Martin ; Jansson, Magnus ; Ottersten, Björn

Author_Institution

Dept. of Signals, Sensors, & Syst., R. Inst. of Technol., Stockholm, Sweden

Volume

3

fYear

1999

fDate

15-19 Mar 1999

Firstpage

1565

Abstract

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)

Keywords

Markov processes; covariance matrices; data models; frequency estimation; maximum likelihood estimation; parameter space methods; signal sampling; ESPRIT; MUSIC; Markov estimator; Markov-like procedure; approximate maximum likelihood algorithm; covariance matrix; large sample equivalence; low-rank system model; observed scalar valued data samples; performance; rank; statistical analysis; subspace based methods; subspace based sinusoidal frequency estimation; vectors; Additive noise; Covariance matrix; Data models; Frequency estimation; Maximum likelihood estimation; Multiple signal classification; Parameter estimation; Sensor systems; Signal processing; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on

Conference_Location

Phoenix, AZ

ISSN

1520-6149

Print_ISBN

0-7803-5041-3

Type

conf

DOI

10.1109/ICASSP.1999.756285

Filename

756285