Classification of Radar Targets Using Invariant Features

Automatic target recognition (ATR) using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit%it the comparison of targets modeled by different numbers of scattering centers. A metric distance metric is used that effectively identifies targets under "real world" conditions that include noise and obscuration.Air Force Inst of Tech Wright-Patterson AFB OH School of Engineering and Management is the author of 'Classification of Radar Targets Using Invariant Features', published 2003 under ISBN 9781423503118 and ISBN 1423503112.