A least-squares based method for autoregressive signals in the presence of noise

Author

Zheng, Wei Xing

Author_Institution

Sch. of Sci., Univ. of Western Sydney, NSW, Australia

Volume

46

Issue

1

fYear

1999

fDate

1/1/1999 12:00:00 AM

Firstpage

81

Lastpage

85

Abstract

The problem of estimating the unknown parameters of an autoregressive (AR) signal observed in white noise, including the signal power and the noise variance, is studied. A new type of least-squares method is developed which is based on a simple technique of estimating the observation noise variance by increasing the degree of the underlying AR model by one. The main feature of the presented method is that the consistent estimates of AR parameters can be directly achieved, with no need to prefilter noisy data or to make any parameter transformation

Keywords

autoregressive processes; least mean squares methods; parameter estimation; signal processing; white noise; AR model; AR parameters; AR signal; autoregressive signals; least-squares based method; noise variance; signal power; white noise; Additive noise; Australia; Autoregressive processes; Equations; Maximum likelihood estimation; Multilevel systems; Parameter estimation; Recursive estimation; Signal processing; White noise;

fLanguage

English

Journal_Title

Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7130

Type

jour

DOI

10.1109/82.749103

Filename

749103