结合奇异值分解的特征基函数分层生成

T. Tanaka, Y. Inasawa, Y. Nishioka, H. Miyashita
{"title":"结合奇异值分解的特征基函数分层生成","authors":"T. Tanaka, Y. Inasawa, Y. Nishioka, H. Miyashita","doi":"10.1109/ICEAA.2016.7731424","DOIUrl":null,"url":null,"abstract":"In this paper, we evaluated the hierarchical generation technique of characteristic basis function combined with singular value decomposition (SVD). This method enables us to analyze a scattering field with a high accuracy with small-scale matrix. By calculating the radar cross section (RCS) of a large conductor cylinder, we evaluated the technique. The solutions of proposed and conventional method correspond well; thus, the effectiveness was verified.","PeriodicalId":434972,"journal":{"name":"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Hierarchical generation of characteristic basis functions combined with singular value decomposition\",\"authors\":\"T. Tanaka, Y. Inasawa, Y. Nishioka, H. Miyashita\",\"doi\":\"10.1109/ICEAA.2016.7731424\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we evaluated the hierarchical generation technique of characteristic basis function combined with singular value decomposition (SVD). This method enables us to analyze a scattering field with a high accuracy with small-scale matrix. By calculating the radar cross section (RCS) of a large conductor cylinder, we evaluated the technique. The solutions of proposed and conventional method correspond well; thus, the effectiveness was verified.\",\"PeriodicalId\":434972,\"journal\":{\"name\":\"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEAA.2016.7731424\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Electromagnetics in Advanced Applications (ICEAA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEAA.2016.7731424","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

本文研究了结合奇异值分解(SVD)的特征基函数分层生成技术。该方法使我们能够用小尺度矩阵高精度地分析散射场。通过计算大型导体圆柱体的雷达截面(RCS),对该技术进行了评价。所提方法与常规方法的解吻合较好;从而验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hierarchical generation of characteristic basis functions combined with singular value decomposition
In this paper, we evaluated the hierarchical generation technique of characteristic basis function combined with singular value decomposition (SVD). This method enables us to analyze a scattering field with a high accuracy with small-scale matrix. By calculating the radar cross section (RCS) of a large conductor cylinder, we evaluated the technique. The solutions of proposed and conventional method correspond well; thus, the effectiveness was verified.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信