The classification of high range resolution radar returns using multiscale features is considered. Because of the characteristics unique to radar signals, such as clutter and sensitivity to viewing angle change, classifiers using features extracted from a single scale do not meet the requirements of non-cooperative target identification (NCTI). We present a hierarchical ARMA model for modeling high range resolution radar signals in multiple scales and apply it to NCTI database containing 5000 test samples and 5000 training samples. We first show that the radar signal at a course scale follows an ARMA process if it follows an ARMA model at a finer scale. The model parameters at different scales are easily computed from the parameters at another scale. Therefore, the hierarchical model allows us to compute spectral features at the coarse scale without adding much computational burden. The multiscale spectral features at five scales are computed using the hierarchical modeling approach, and are classified by a minimum distance classifier. The multiscale classifier is applied to both poorly aligned data and better aligned data. For both data sets, about 95 percent of the radar returns were correctly classified, showing that the multiscale classifier is robust to misalignment.
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