{"title":"Linear and non linear tomographic approaches for imaging dielectric targets embedded in an inhomogeneous medium","authors":"F. Soldovicri, I. Catapano, L. Crocco, R. Persico","doi":"10.1109/AGPR.2005.1487871","DOIUrl":null,"url":null,"abstract":"In this paper we compare two different tomographic approaches for imaging dielectric objects embedded in an inhomogeneous scenario. The first one exploits the Born Approximation (BA) and belongs to the class of linearized methods, while the second one is a non-linear iterative approach. As far as the first one is concerned, only qualitative information can be obtained, but these can be acquired in quasi real time, even for large investigated domains, provided that some off-line computation is performed. On the other hand, the non-linear inversion technique can give more detailed information, but in any case it involves a larger computational burden, owing to its iterative nature. Numerical examples assess robustness of both the approaches against noise on data.","PeriodicalId":272364,"journal":{"name":"Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar, 2005. IWAGPR 2005.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar, 2005. IWAGPR 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AGPR.2005.1487871","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper we compare two different tomographic approaches for imaging dielectric objects embedded in an inhomogeneous scenario. The first one exploits the Born Approximation (BA) and belongs to the class of linearized methods, while the second one is a non-linear iterative approach. As far as the first one is concerned, only qualitative information can be obtained, but these can be acquired in quasi real time, even for large investigated domains, provided that some off-line computation is performed. On the other hand, the non-linear inversion technique can give more detailed information, but in any case it involves a larger computational burden, owing to its iterative nature. Numerical examples assess robustness of both the approaches against noise on data.