Detection and classification of noisy AR and ARMA processes

Abstract

The paper focuses on the detection and the classification of noisy AR and ARMA processes. These two kinds of processes cannot be distinguished by means of their second-order statistics, since they are Spectrally Equivalent (SE). Higher-order statistics are shown to be an efficient tool for their detection. A Neyman-Pearson (NP) test, based on these higher-order statistics, is then studied. The performance of the NP test provides a reference for comparing suboptimal detector performances.