ARMA modeling in multichannel filter banks with applications to radar signal analysis

Abstract

Considers the classification of radar signals by using stochastic models at different scales. The signal at a different scale is modeled by a hierarchical autoregressive moving average (ARMA) model, and the features at coarse scales are extracted from the model without performing expensive filtering operations. The hierarchical modeling can increase the accuracy of radar signal classification by exploiting features at different scales. For radar signal classification, model parameters at five different scales obtained by hierarchical modeling are used as features. A minimum distance classifier is implemented, and is tested on real aperture radar signals.