DocumentCode :
3144351
Title :
A unified view of non-stationary sinusoidal parameter estimation methods using signal derivatives
Author :
Hamilton, Blaine ; Depalle, Philippe
Author_Institution :
Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada
fYear :
2012
fDate :
25-30 March 2012
Firstpage :
369
Lastpage :
372
Abstract :
In this paper, we present a unified view of three non-stationary sinusoidal parameter estimation methods which are based on taking linear transforms of a signal and its derivatives. These methods, the Distribution Derivative Method (DDM), the Generalized Derivative Method (GDM), and the Generalized Reassignment Method (GRM), are shown to be subcases of a more general method which results in a system of linear equations from which we can solve for the parameter estimators. While the GDM and GRM are known to be theoretically equivalent, we show that they are also equivalent to the DDM in one special case. Matrix formulations are established for the GDM and GRM with a polynomial log-amplitude, polynomial phase sinusoidal signal model, and a bias in previous frequency slope estimators is explicitly demonstrated.
Keywords :
polynomial matrices; signal processing; transforms; distribution derivative method; frequency slope estimator; generalized derivative method; generalized reassignment method; linear transforms; matrix formulation; nonstationary sinusoidal parameter estimation method; polynomial log-amplitude; polynomial phase sinusoidal signal model; signal derivative; Estimation; Frequency estimation; Frequency modulation; Mathematical model; Polynomials; Transforms; Parameter estimation; derivative method; non-stationary sinusoidal analysis; reassignment method;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on
Conference_Location :
Kyoto
ISSN :
1520-6149
Print_ISBN :
978-1-4673-0045-2
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2012.6287893
Filename :
6287893
Link To Document :
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