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Nou-cooperative target classification using hierarchic id modeling of high range resolution radar signatures

机译：使用高分辨率雷达签名的分层id建模的非合作目标分类

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摘要

The classification of high range resolution radar returns using muitiscale features is considered. Because of characteristics unique to radar signals, such as clutter and sensitivity to viewing angle change, classifiers using feature 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 NOTI database containing 5000 test samples and 5000 training samples. We first show that the radar signal at a coarse 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 muitiscale spectral features a: five scales are computed using the hierarchical modeling approach, and are classified by a minimum distance classifier The muitiscale classifier is applied to both poorly aligned data and better aligned data. For both data sets, about 95percent of the radar returns were correctly classified, showing that the multiscale classifier is robust to misalignment

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