Estimation of quasiperiodic signal parameters by means of dynamic signal models

Author

Gruber, Peter ; Tödtli, Jürg

Author_Institution

Corporate Res. & Dev., Landis & Gyr Betriebs AG, Zug, Switzerland

Volume

42

Issue

3

fYear

1994

fDate

3/1/1994 12:00:00 AM

Firstpage

552

Lastpage

562

Abstract

The problem of estimating the parameters of a quasiperiodic signal consisting of a sum of a given number of sinusoidal signals with known frequencies but unknown time-varying amplitudes and phases superimposed by some additive noise sources is treated in this paper. Different estimation techniques that solve the problem are presented in this paper. The reason to do that is twofold: (1) to present the derivation of a new estimator for quasiperiodic signals, which is based on Kalman filtering theory and a random walk model, and to illustrate its structure and parametrization; and (2) to compare the new estimator with another Kalman filter-based estimator that uses an “oscillator model” and with the more classical running recursive discrete Fourier series method

Keywords

Kalman filters; fast Fourier transforms; filtering and prediction theory; noise; parameter estimation; signal processing; Kalman filtering theory; additive noise sources; dynamic signal models; estimation techniques; oscillator model; parameter estimation; quasiperiodic signal; random walk model; running recursive discrete Fourier series; sinusoidal signals; time-varying amplitudes; time-varying phases; Additive noise; Amplitude estimation; Filtering theory; Fourier series; Frequency estimation; Kalman filters; Oscillators; Parameter estimation; Phase estimation; Recursive estimation;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.277847

Filename

277847