{"title":"利用边界条件约束增强导电目标线性采样成像","authors":"Matthew J. Burfeindt, H. Alqadah","doi":"10.1109/RAPID49481.2020.9195708","DOIUrl":null,"url":null,"abstract":"We present a new formulation of the Linear Sampling Method (LSM) for imaging conducting targets from spatially sparse data acquisitions. The technique mitigates the lack of spatial channels by introducing a priori information into the problem formulation related to the electric field boundary conditions on the unknown target boundary. We apply the proposed technique to simulated data and demonstrate improved image fidelity relative to the standard LSM.","PeriodicalId":220244,"journal":{"name":"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Enhancement of Linear Sampling Method imaging of conducting targets using a boundary condition constraint\",\"authors\":\"Matthew J. Burfeindt, H. Alqadah\",\"doi\":\"10.1109/RAPID49481.2020.9195708\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a new formulation of the Linear Sampling Method (LSM) for imaging conducting targets from spatially sparse data acquisitions. The technique mitigates the lack of spatial channels by introducing a priori information into the problem formulation related to the electric field boundary conditions on the unknown target boundary. We apply the proposed technique to simulated data and demonstrate improved image fidelity relative to the standard LSM.\",\"PeriodicalId\":220244,\"journal\":{\"name\":\"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAPID49481.2020.9195708\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAPID49481.2020.9195708","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Enhancement of Linear Sampling Method imaging of conducting targets using a boundary condition constraint
We present a new formulation of the Linear Sampling Method (LSM) for imaging conducting targets from spatially sparse data acquisitions. The technique mitigates the lack of spatial channels by introducing a priori information into the problem formulation related to the electric field boundary conditions on the unknown target boundary. We apply the proposed technique to simulated data and demonstrate improved image fidelity relative to the standard LSM.