Title :
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
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;
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
DOI :
10.1109/SSAP.1996.534818
