{"title":"运动目标的电磁分析与微运动参数提取","authors":"Tian Yang, Shaoran Wang, Mengmeng Li, Rushan Chen","doi":"10.1109/csrswtc50769.2020.9372628","DOIUrl":null,"url":null,"abstract":"We propose an effective domain decomposition method with spherical equivalence surface for the electromagnetic modeling of moving targets from near coupling to far coupling region. Both efficiency and computation accuracy are enhanced highly. With the simulated far fields, we employ the deep learning to extract the micro-motion parameters corresponding to different time-frequency distribution images accurately. The experiment confirms the validity of the proposed method for modeling and extracting the micro-motion parameters of moving targets.","PeriodicalId":207010,"journal":{"name":"2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC)","volume":"2572 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Electromagnetic Analysis and Micro-motion Parameters Extraction of Moving Targets\",\"authors\":\"Tian Yang, Shaoran Wang, Mengmeng Li, Rushan Chen\",\"doi\":\"10.1109/csrswtc50769.2020.9372628\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an effective domain decomposition method with spherical equivalence surface for the electromagnetic modeling of moving targets from near coupling to far coupling region. Both efficiency and computation accuracy are enhanced highly. With the simulated far fields, we employ the deep learning to extract the micro-motion parameters corresponding to different time-frequency distribution images accurately. The experiment confirms the validity of the proposed method for modeling and extracting the micro-motion parameters of moving targets.\",\"PeriodicalId\":207010,\"journal\":{\"name\":\"2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC)\",\"volume\":\"2572 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/csrswtc50769.2020.9372628\",\"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 Cross Strait Radio Science & Wireless Technology Conference (CSRSWTC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/csrswtc50769.2020.9372628","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electromagnetic Analysis and Micro-motion Parameters Extraction of Moving Targets
We propose an effective domain decomposition method with spherical equivalence surface for the electromagnetic modeling of moving targets from near coupling to far coupling region. Both efficiency and computation accuracy are enhanced highly. With the simulated far fields, we employ the deep learning to extract the micro-motion parameters corresponding to different time-frequency distribution images accurately. The experiment confirms the validity of the proposed method for modeling and extracting the micro-motion parameters of moving targets.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
