{"title":"基于稀疏表示的自适应干扰抑制","authors":"Yishu Shi, F. Ge, Ying Chen, Sui-ling Ren","doi":"10.23919/OCEANS.2015.7401918","DOIUrl":null,"url":null,"abstract":"Passive sources localization in the presence of strong interferences is generally a difficult problem. A sparse-representation-based adaptive interference suppression (SRAIS) method is proposed in this paper for interference suppression and bearing estimation, which can reduce the power loss of the TOI signal and have more accurate direction-of-arrival (DOA) estimation, especially when the input powers of the TOI signal and the interferences are at the almost same level. Simulation and experimental results are also given.","PeriodicalId":403976,"journal":{"name":"OCEANS 2015 - MTS/IEEE Washington","volume":"42 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sparse-representation-based adaptive interference suppression\",\"authors\":\"Yishu Shi, F. Ge, Ying Chen, Sui-ling Ren\",\"doi\":\"10.23919/OCEANS.2015.7401918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Passive sources localization in the presence of strong interferences is generally a difficult problem. A sparse-representation-based adaptive interference suppression (SRAIS) method is proposed in this paper for interference suppression and bearing estimation, which can reduce the power loss of the TOI signal and have more accurate direction-of-arrival (DOA) estimation, especially when the input powers of the TOI signal and the interferences are at the almost same level. Simulation and experimental results are also given.\",\"PeriodicalId\":403976,\"journal\":{\"name\":\"OCEANS 2015 - MTS/IEEE Washington\",\"volume\":\"42 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"OCEANS 2015 - MTS/IEEE Washington\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/OCEANS.2015.7401918\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"OCEANS 2015 - MTS/IEEE Washington","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/OCEANS.2015.7401918","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Passive sources localization in the presence of strong interferences is generally a difficult problem. A sparse-representation-based adaptive interference suppression (SRAIS) method is proposed in this paper for interference suppression and bearing estimation, which can reduce the power loss of the TOI signal and have more accurate direction-of-arrival (DOA) estimation, especially when the input powers of the TOI signal and the interferences are at the almost same level. Simulation and experimental results are also given.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
