{"title":"Performance analysis of Volterra kernel estimators with Gaussian inputs","authors":"A. Redfern, G.T. Zhou","doi":"10.1109/HOST.1997.613508","DOIUrl":null,"url":null,"abstract":"The focus of this paper is on Volterra nonlinear system identification from input-output data. When the system is linear-quadratic and the input is Gaussian, closed-form expressions for the kernels were derived by Tick (1961) based on input-output cross-cumulants. However, there have been no known variance expressions for the kernel estimates. In this paper, we analyze the performance of the first- and second-order kernel estimates when the input is zero-mean white Gaussian, and the additive noise has unknown color and distribution. Closed-form variance expressions are presented and verified by simulations.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","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.1997.613508","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 focus of this paper is on Volterra nonlinear system identification from input-output data. When the system is linear-quadratic and the input is Gaussian, closed-form expressions for the kernels were derived by Tick (1961) based on input-output cross-cumulants. However, there have been no known variance expressions for the kernel estimates. In this paper, we analyze the performance of the first- and second-order kernel estimates when the input is zero-mean white Gaussian, and the additive noise has unknown color and distribution. Closed-form variance expressions are presented and verified by simulations.
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