{"title":"Recursive estimation algorithm for FIR systems using the 3rd and 4th order cumulants","authors":"Hyoungill Kim, Bum-Ki Jeon, Taewon Yang, K. Sung","doi":"10.1109/HOST.1997.613525","DOIUrl":null,"url":null,"abstract":"A recursive estimation algorithm for FIR systems is proposed using the 3rd and 4th order cumulants. From the 3rd and 4th order cumulants relationship, we construct a certain matrix form whose entry consists of the system output sequence. Using this matrix form, the proposed recursive algorithm is developed by the overdetermined recursive instrumental variable (ORIV) method. The proposed algorithm provides improved estimation accuracy when additive Gaussian noise is present and can be applied to a time varying system as well. Simulation results are presented to compare the performance with other HOS-based algorithms.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"131 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HOST.1997.613525","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
A recursive estimation algorithm for FIR systems is proposed using the 3rd and 4th order cumulants. From the 3rd and 4th order cumulants relationship, we construct a certain matrix form whose entry consists of the system output sequence. Using this matrix form, the proposed recursive algorithm is developed by the overdetermined recursive instrumental variable (ORIV) method. The proposed algorithm provides improved estimation accuracy when additive Gaussian noise is present and can be applied to a time varying system as well. Simulation results are presented to compare the performance with other HOS-based algorithms.