Title :
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
fDate :
6/1/2007 12:00:00 AM
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 mmax, 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;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2007.893966
