{"title":"基于粗糙等效聚类的卫星通信欠定盲分离","authors":"Chengjie Li, Lidong Zhu, Zhongqiang Luo","doi":"10.1109/ISNCC.2018.8530911","DOIUrl":null,"url":null,"abstract":"The problem of underdetermined blind source separation for satellite communications is proposed in this paper. In underdetermined blind separation, people suppose the source is sparse and the number of source signals is known when they estimate the mixture matrix. In fact, the sparsity is often not satisfied and the number of source signals is unknown. This paper presents a novel Rough Set algorithm (RS algorithm) based on rough set theory, which can get the source signal sparse points and accurately estimate the number of sources and the mixture matrix respectively, by which source signals can be reconstructed. The last simulations show the good performance of the paper's algorithm.","PeriodicalId":313846,"journal":{"name":"2018 International Symposium on Networks, Computers and Communications (ISNCC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Underdetermined Blind Separation Via Rough Equivalence Clustering for Satellite Communications\",\"authors\":\"Chengjie Li, Lidong Zhu, Zhongqiang Luo\",\"doi\":\"10.1109/ISNCC.2018.8530911\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of underdetermined blind source separation for satellite communications is proposed in this paper. In underdetermined blind separation, people suppose the source is sparse and the number of source signals is known when they estimate the mixture matrix. In fact, the sparsity is often not satisfied and the number of source signals is unknown. This paper presents a novel Rough Set algorithm (RS algorithm) based on rough set theory, which can get the source signal sparse points and accurately estimate the number of sources and the mixture matrix respectively, by which source signals can be reconstructed. The last simulations show the good performance of the paper's algorithm.\",\"PeriodicalId\":313846,\"journal\":{\"name\":\"2018 International Symposium on Networks, Computers and Communications (ISNCC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Symposium on Networks, Computers and Communications (ISNCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISNCC.2018.8530911\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Symposium on Networks, Computers and Communications (ISNCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISNCC.2018.8530911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Underdetermined Blind Separation Via Rough Equivalence Clustering for Satellite Communications
The problem of underdetermined blind source separation for satellite communications is proposed in this paper. In underdetermined blind separation, people suppose the source is sparse and the number of source signals is known when they estimate the mixture matrix. In fact, the sparsity is often not satisfied and the number of source signals is unknown. This paper presents a novel Rough Set algorithm (RS algorithm) based on rough set theory, which can get the source signal sparse points and accurately estimate the number of sources and the mixture matrix respectively, by which source signals can be reconstructed. The last simulations show the good performance of the paper's algorithm.