A. S. Turk, P. Ozkan-Bakbak, L. Durak-Ata, Melek Orhan, Mehmet Unal
{"title":"Reconstruction of through-the-wall imaging radar signals by compressive sensing","authors":"A. S. Turk, P. Ozkan-Bakbak, L. Durak-Ata, Melek Orhan, Mehmet Unal","doi":"10.1109/SPS.2015.7168258","DOIUrl":null,"url":null,"abstract":"Compressive sensing (CS) enables the reconstruction of ground-penetrating radar (GPR) signals by processing in a compressed region with a few samples instead of taking all signal samples. In this study, microwave radar signals which have been produced in microwave laboratory are processed by CS. Reflection data samples which are produced between 0.1 GHz-15 GHz frequency band with 0.074 GHz increments are taken randomly at ¼, ½, ¾ of quantity and reconstructed via convex optimization by CS. Considering the Fourier coefficients of the signals, 50 and 100 sparse Fourier coefficients are taken to analysis the difference between them.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Signal Processing Symposium (SPSympo)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPS.2015.7168258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Compressive sensing (CS) enables the reconstruction of ground-penetrating radar (GPR) signals by processing in a compressed region with a few samples instead of taking all signal samples. In this study, microwave radar signals which have been produced in microwave laboratory are processed by CS. Reflection data samples which are produced between 0.1 GHz-15 GHz frequency band with 0.074 GHz increments are taken randomly at ¼, ½, ¾ of quantity and reconstructed via convex optimization by CS. Considering the Fourier coefficients of the signals, 50 and 100 sparse Fourier coefficients are taken to analysis the difference between them.