<title>Superresolution HRR ATR performance with HDVI</title>

Abstract

A goal of super-resolution, in addition to improving probability of correct classification (Pcc) in automatic target recognition systems, is to reduce radar resource requirements in achieving a given Pcc. These studies address the MIT Lincoln Laboratory 1-D template-based ATR algorithm that was developed and tested on super-resolved high range resolution (HRR) profiles formed from synthetic aperture radar (SAR) images of targets taken from the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Previous studies on HRR ATR demonstrated encouraging results for recognition of stationary targets from their HRR profiles, although the low probability of correct classification dictates a large margin of improvement in Pcc is needed before the system can be operational. In this work, a super- resolution technique known as High Definition Vector Imaging (HDVI) is applied to the HRR profiles before the profiles are passed through the ATR classification. The new 1-D ATR system using super-resolved HRR demonstrates significantly improved target recognition compared to previous 1-D ATR systems that use conventional image processing techniques. This paper discusses the improvement in HRR ATR performance in terms of radar resource requirements as a result of applying HDVI.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.