High-definition vector imaging for synthetic aperture radar

Abstract

High-definition vector imaging (HDVI) is a data-adaptive approach to synthetic aperture radar (SAR) image reconstruction based on superresolution techniques originally developed for passive sensor arrays. The goal is to produce more informative, higher resolution imagery for improving target recognition with UHF and millimeter-wave SAR. Algorithms presented here include 2-D minimum-variance techniques based on the MLM (Capon) algorithm and a 2-D version of the MUSIC algorithm. A comparison of techniques via simulation is provided. Results are presented for wideband rail SAR measurements of reflectors in foliage, demonstrating resolution improvement and clutter rejection. Also, results of processing data from an airborne millimeter-wave SAR demonstrate improved resolution and speckle reduction. The "Vector" aspect, i.e., the incorporation of non-pointlike scattering models to enable characterization of scattering mechanisms, is briefly discussed.