<title>Automatic target recognition with intensity- and distortion-invariant hybrid composite filters</title>

Abstract

Realistic scenery not only has out-of-plane rotation as a distortion but may also contain extreme intra-class intensity variations. We apply a distortion-invariant correlation filter bank known at the Hybrid Composite filter bank to realistic scenery. Hybrid Composite filters are a unification of several well known synthetic discriminant functions which include the classical synthetic discriminant function, Minimum Average Correlation Energy filter, Linear Phase Coefficient Composite filter and Linear Phase Response synthetic discriminant function. it has been demonstrated that Hybrid Composite filter detection and discrimination performance is better than the individual filter types that they are formed from. Hybrid Composite filters act to perform morphological transformations on distorted target and clutter images. The resulting small set of feature shapes can be used to estimate a local peak to sidelobe ratio which is intensity-invariant. Only target training images are used to design the filter bank. Clutter is inherently discriminated against because of the complex phasor relationships used in Hybrid Composite filter design. No scene segmentation is necessary to remove unwanted regions. We demonstrate best case and worst case performance with a set of vehicle images.