Deep learning for classification of targets using low-frequency ultra-wideband synthetic aperture radar imagery

Abstract

In this paper, we present recent investigations by the U.S. Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL) into using low-frequency ultra-wideband (UWB) synthetic aperture radar (SAR) to detect obscured and buried targets. In particular, we investigate features that potentially discriminate between the target and clutter classes with the aim of classifying multiple target classes. In addition to the time- or spatial-domain complex data responses derived from the targets’ signatures, we consider the variations of the targets’ responses with changes across multiple polarization channels, viewing aspect angles, and frequency spectra. We apply deep neural networks to exploit these discrimination features extracted from SAR signatures of targets-of-interest and clutter.