AR spectral estimation with randomly missed observations

Author

Mirsaidi, Sina ; Oksman, Jacques

Author_Institution

Service des Mesures, Ecole Superieure d´´Electr., Gif-sur-Yvette, France

fYear

1996

fDate

24-26 Jun 1996

Firstpage

52

Lastpage

55

Abstract

This paper represents a new spectral estimation method for time series with missed observations. An auto-regressive (AR) modeling approach is adopted. The AR parameters are estimated by optimizing a weighted mean-square error criterion. The method can be used in real-time adaptive contexts where the AR parameters are time varying. In general both regularly and randomly missed observations can be handled by this method. The spectral estimates are compared to those obtained by well known AR parameter estimators used in the cases where none of the signal samples is missed. The performance of the method is illustrated by some numerical examples

Keywords

autoregressive processes; least mean squares methods; parameter estimation; random processes; spectral analysis; time series; AR spectral estimation; auto-regressive modeling approach; performance; randomly missed observations; real-time adaptive contexts; time series; weighted mean-square error criterion; Data compression; Filtering; H infinity control; Kalman filters; Loss measurement; Maximum likelihood estimation; Nonlinear filters; Optimization methods; Recursive estimation; Resonance light scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 1996. Proceedings., 8th IEEE Signal Processing Workshop on (Cat. No.96TB10004

Conference_Location

Corfu

Print_ISBN

0-8186-7576-4

Type

conf

DOI

10.1109/SSAP.1996.534818

Filename

534818