{"title":"High-definition vector imaging for synthetic aperture radar","authors":"G. Benitz","doi":"10.1109/ACSSC.1997.679095","DOIUrl":null,"url":null,"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.","PeriodicalId":240431,"journal":{"name":"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"58","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.1997.679095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 58
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.
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