{"title":"变换域的创新CS成像方法","authors":"T. Moriyama, N. Anselmi, G. Oliveri, A. Massa","doi":"10.1109/CAMA.2014.7003312","DOIUrl":null,"url":null,"abstract":"The solution of linear microwave imaging problems is considered in this work through innovative classes of Compressive Sensing (CS) methods. More in detail, the formulation of the inversion process in transformed domains with sparseness-regularized formulations is considered. Representative numerical examples illustrating the potentialities and limitations of the arising CS inversion approaches are reported.","PeriodicalId":409536,"journal":{"name":"2014 IEEE Conference on Antenna Measurements & Applications (CAMA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Innovative CS imaging methods in transformed domains\",\"authors\":\"T. Moriyama, N. Anselmi, G. Oliveri, A. Massa\",\"doi\":\"10.1109/CAMA.2014.7003312\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The solution of linear microwave imaging problems is considered in this work through innovative classes of Compressive Sensing (CS) methods. More in detail, the formulation of the inversion process in transformed domains with sparseness-regularized formulations is considered. Representative numerical examples illustrating the potentialities and limitations of the arising CS inversion approaches are reported.\",\"PeriodicalId\":409536,\"journal\":{\"name\":\"2014 IEEE Conference on Antenna Measurements & Applications (CAMA)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Conference on Antenna Measurements & Applications (CAMA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMA.2014.7003312\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Conference on Antenna Measurements & Applications (CAMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMA.2014.7003312","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Innovative CS imaging methods in transformed domains
The solution of linear microwave imaging problems is considered in this work through innovative classes of Compressive Sensing (CS) methods. More in detail, the formulation of the inversion process in transformed domains with sparseness-regularized formulations is considered. Representative numerical examples illustrating the potentialities and limitations of the arising CS inversion approaches are reported.