Noncooperative target classification using hierarchical modeling of high-range resolution radar signatures

The classiication of High Range Resolution (HRR) radar signatures using multi-scale features is considered. We present a hierarchical autoregressive moving average (ARMA) model for modeling HRR radar signals at multiple scales, and use spectral features extracted from the model for classifying radar signatures. First, we show that the radar signal at a diierent scale follows an ARMA process if it is an ARMA process at the observed scale. Then an algorithm to estimate model parameters and power spectral density function at diierent scales using model parameters at the observed scale is presented. A feature set composed of spectral peaks is extracted from the estimated spectral density function using multi-scale ARMA models. For HRR radar signature classiication, multi-spectral features extracted from ve diierent scales are used, and a minimum distance classiier with multiple prototypes is used to classify HRR data. The multi-scale classiier is applied to two HRR radar data sets. Each data set contains 2500 test samples and 2500 training samples in ve classes. For both data sets, about 95 percent of the radar returns are correctly classiied.