Y. Kuznetsov, A. Baev, M. Konovalyuk, A. Gorbunova, J. Russer
{"title":"Independent Component Analysis of the Cyclostationary Signals in the Transmission Line","authors":"Y. Kuznetsov, A. Baev, M. Konovalyuk, A. Gorbunova, J. Russer","doi":"10.1109/EMCEurope51680.2022.9901014","DOIUrl":null,"url":null,"abstract":"The blind separation of the cyclostationary signals using a single channel measurement in transmission line is proposed. The waveform identification of the partial pulse responses is evaluated for additively mixed sources of the data, inter-symbol interference and crosstalk. A matrix model of a composed random vectors is considered. The proposed estimation procedure is based on pre-processing using principal component analysis of measured data by digital oscilloscope and following independent component analysis. The component analysis allows diagnostics of signal integrity using eye-diagram patterns in additional dimension of sources and interference.","PeriodicalId":268262,"journal":{"name":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Symposium on Electromagnetic Compatibility – EMC Europe","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMCEurope51680.2022.9901014","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 blind separation of the cyclostationary signals using a single channel measurement in transmission line is proposed. The waveform identification of the partial pulse responses is evaluated for additively mixed sources of the data, inter-symbol interference and crosstalk. A matrix model of a composed random vectors is considered. The proposed estimation procedure is based on pre-processing using principal component analysis of measured data by digital oscilloscope and following independent component analysis. The component analysis allows diagnostics of signal integrity using eye-diagram patterns in additional dimension of sources and interference.