{"title":"变换域CPtNLMS算法","authors":"K. Wagner, M. Doroslovački","doi":"10.1109/CISS.2013.6552258","DOIUrl":null,"url":null,"abstract":"The concept of self-orthogonalizing adaptation is extended from the least mean square algorithm to the general case of complex proportionate type normalized least mean square algorithms. The derived algorithm requires knowledge of the input signal's covariance matrix. Implementation of the algorithm using a fixed transform such as the discrete cosine transform or discrete wavelet transform is presented for applications in which the input signal's covariance matrix is unknown.","PeriodicalId":268095,"journal":{"name":"2013 47th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transform domain CPtNLMS algorithms\",\"authors\":\"K. Wagner, M. Doroslovački\",\"doi\":\"10.1109/CISS.2013.6552258\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The concept of self-orthogonalizing adaptation is extended from the least mean square algorithm to the general case of complex proportionate type normalized least mean square algorithms. The derived algorithm requires knowledge of the input signal's covariance matrix. Implementation of the algorithm using a fixed transform such as the discrete cosine transform or discrete wavelet transform is presented for applications in which the input signal's covariance matrix is unknown.\",\"PeriodicalId\":268095,\"journal\":{\"name\":\"2013 47th Annual Conference on Information Sciences and Systems (CISS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 47th Annual Conference on Information Sciences and Systems (CISS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CISS.2013.6552258\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 47th Annual Conference on Information Sciences and Systems (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CISS.2013.6552258","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The concept of self-orthogonalizing adaptation is extended from the least mean square algorithm to the general case of complex proportionate type normalized least mean square algorithms. The derived algorithm requires knowledge of the input signal's covariance matrix. Implementation of the algorithm using a fixed transform such as the discrete cosine transform or discrete wavelet transform is presented for applications in which the input signal's covariance matrix is unknown.