The Burg algorithm for segments

Author

De Waele, Stijn ; Broersen, Piet M T

Author_Institution

Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands

Volume

48

Issue

10

fYear

2000

fDate

10/1/2000 12:00:00 AM

Firstpage

2876

Lastpage

2880

Abstract

In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes

Keywords

autoregressive processes; parameter estimation; time series; autoregressive model; averaging; bias; estimated parameters; segments; time series; uninterrupted measurement; variance; weighted Burg algorithm; Clutter; Data analysis; Geophysical measurements; Least squares approximation; Object detection; Parameter estimation; Radar applications; Reflection; Signal processing; Time measurement;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.869039

Filename

869039