{"title":"Weighted least square based frequency estimation algorithm","authors":"R. Punchalard","doi":"10.1109/IEECON.2017.8075865","DOIUrl":null,"url":null,"abstract":"The LMS-based indirect frequency estimation algorithm (IFE) is reformulated using weighted least-square error criterion. Theoretical analyses for steady state bias and mean square error (MSE) are addressed. It has been shown that the proposed algorithm outperforms the conventional LMS-based algorithms in terms of convergence speed at the same value of MSE.","PeriodicalId":196081,"journal":{"name":"2017 International Electrical Engineering Congress (iEECON)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Electrical Engineering Congress (iEECON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEECON.2017.8075865","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The LMS-based indirect frequency estimation algorithm (IFE) is reformulated using weighted least-square error criterion. Theoretical analyses for steady state bias and mean square error (MSE) are addressed. It has been shown that the proposed algorithm outperforms the conventional LMS-based algorithms in terms of convergence speed at the same value of MSE.
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