{"title":"基于改进单PARAFAC分解的盲MIMO系统估计","authors":"Yuanning Yu, A. Petropulu","doi":"10.1109/ISSPIT.2005.1577078","DOIUrl":null,"url":null,"abstract":"We consider the problem of frequency domain identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed by the authors that uses PARAFAC decomposition of a tensor that is formed based on higher-order statistics of the system output. The approach of Y. Yu and A.P. Petropulu, 2005, utilizes only one slice of the output tensor to recover one row of the system response matrix. We proposed an approach that fully exploits the information in the output tensor, and as a result achieves lower error values. The proposed modification renders the method applicable to systems with more inputs than outputs","PeriodicalId":421826,"journal":{"name":"Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved single PARAFAC decomposition based blind MIMO system estimation\",\"authors\":\"Yuanning Yu, A. Petropulu\",\"doi\":\"10.1109/ISSPIT.2005.1577078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the problem of frequency domain identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed by the authors that uses PARAFAC decomposition of a tensor that is formed based on higher-order statistics of the system output. The approach of Y. Yu and A.P. Petropulu, 2005, utilizes only one slice of the output tensor to recover one row of the system response matrix. We proposed an approach that fully exploits the information in the output tensor, and as a result achieves lower error values. The proposed modification renders the method applicable to systems with more inputs than outputs\",\"PeriodicalId\":421826,\"journal\":{\"name\":\"Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-12-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT.2005.1577078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Fifth IEEE International Symposium on Signal Processing and Information Technology, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2005.1577078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved single PARAFAC decomposition based blind MIMO system estimation
We consider the problem of frequency domain identification of a multiple-input multiple-output (MIMO) system driven by white, mutually independent unobservable inputs. In particular, we improve upon a method recently proposed by the authors that uses PARAFAC decomposition of a tensor that is formed based on higher-order statistics of the system output. The approach of Y. Yu and A.P. Petropulu, 2005, utilizes only one slice of the output tensor to recover one row of the system response matrix. We proposed an approach that fully exploits the information in the output tensor, and as a result achieves lower error values. The proposed modification renders the method applicable to systems with more inputs than outputs