{"title":"A new radar target classification approach based on polarimetric high range resolution","authors":"Jiang Yicheng, L. Yongtan, Yu Ping","doi":"10.1109/ICR.1996.573793","DOIUrl":null,"url":null,"abstract":"A new millimeter-wave (MMW) radar target classification approach has been proposed using polarimetric information to obtain stable amplitudes of range profiles, and neural learning to extract angle invariant features of range profiles. The means of the polarimetric processing for reducing the speckle can enhance ability to discriminate targets. Compared with conventional approaches, the subclass features obtained carry more information due to the neural learning and thus make the correctness of target classification higher. The simulation results have verified the validity of this approach.","PeriodicalId":144063,"journal":{"name":"Proceedings of International Radar Conference","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of International Radar Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICR.1996.573793","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
A new millimeter-wave (MMW) radar target classification approach has been proposed using polarimetric information to obtain stable amplitudes of range profiles, and neural learning to extract angle invariant features of range profiles. The means of the polarimetric processing for reducing the speckle can enhance ability to discriminate targets. Compared with conventional approaches, the subclass features obtained carry more information due to the neural learning and thus make the correctness of target classification higher. The simulation results have verified the validity of this approach.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
