T. Fromenteze, O. Yurduseven, Philipp del Hougne, David Smith
{"title":"Principal component analysis for microwave and millimeter wave computational imaging","authors":"T. Fromenteze, O. Yurduseven, Philipp del Hougne, David Smith","doi":"10.1109/iWAT54881.2022.9811037","DOIUrl":null,"url":null,"abstract":"The development of new imaging systems based on computational techniques greatly alleviate the hardware constraints usually encountered in these domains. The limitations associated with the use of highly redundant active chains are thus substituted by the development of radiating systems allowing the transfer of these limitations to the software layer. Faced with the large volumes of data to be processed, it is still necessary to propose numerical techniques to limit the size of the problems to be solved while ensuring that the imaging results remain exploitable for the targeted applications. Principal component analysis offers interesting insights in such a context.","PeriodicalId":106416,"journal":{"name":"2022 International Workshop on Antenna Technology (iWAT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Workshop on Antenna Technology (iWAT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iWAT54881.2022.9811037","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
The development of new imaging systems based on computational techniques greatly alleviate the hardware constraints usually encountered in these domains. The limitations associated with the use of highly redundant active chains are thus substituted by the development of radiating systems allowing the transfer of these limitations to the software layer. Faced with the large volumes of data to be processed, it is still necessary to propose numerical techniques to limit the size of the problems to be solved while ensuring that the imaging results remain exploitable for the targeted applications. Principal component analysis offers interesting insights in such a context.