Order Estimation and Discrimination Between Stationary and Time-Varying (TVAR) Autoregressive Models

Author

Abramovich, Yuri I. ; Spencer, Nicholas K. ; Turley, Michael D E

Author_Institution

Intelligence, Surveillance, & Reconnaissance Div., Defence Sci. & Technol. Organ., Adelaide, SA

Volume

55

Issue

6

fYear

2007

fDate

6/1/2007 12:00:00 AM

Firstpage

2861

Lastpage

2876

Abstract

For a set of T independent observations of the same N-variate correlated Gaussian process, we derive a method of estimating the order of an autoregressive (AR) model of this process, regardless of its stationary or time-varying nature. We also derive a test to discriminate between stationary AR models of order m,AR(m), and time-varying autoregressive models of order m,TVAR(m). We demonstrate that within this technique the number T of independent identically distributed data samples required for order estimation and discrimination just exceeds the maximum possible order m_max, which in many cases is significantly fewer than the dimension of the problem N

Keywords

Gaussian processes; autoregressive processes; correlation methods; signal sampling; N-variate correlated Gaussian process; independently identically distributed data samples; order estimation; stationary models; time-varying autoregressive models; Australia; Covariance matrix; Gaussian processes; Interference; Maximum likelihood detection; Maximum likelihood estimation; Object detection; Parameter estimation; Testing; Training data; Adaptive processing; autoregressive (AR); nonstationary interference; time-varying;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2007.893966

Filename

4203034