{"title":"利用循环平稳性提取近周期信号","authors":"A. V. Dandawate, G. Giannakis","doi":"10.1109/ICASSP.1994.389851","DOIUrl":null,"url":null,"abstract":"Extraction of almost periodic signals from their noisy observations is accomplished by exploiting cyclostationarity. The additive noise is allowed to be generally cyclostationary with unknown distribution. Consistency of the proposed estimators is proved and their asymptotic properties are presented. Further, adaptive algorithms are employed for tracking possible time-variations in the parameters of the almost periodic signal. Finally, the proposed methods are tested via simulations.<<ETX>>","PeriodicalId":290798,"journal":{"name":"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Extraction of almost periodic signals using cyclostationarity\",\"authors\":\"A. V. Dandawate, G. Giannakis\",\"doi\":\"10.1109/ICASSP.1994.389851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Extraction of almost periodic signals from their noisy observations is accomplished by exploiting cyclostationarity. The additive noise is allowed to be generally cyclostationary with unknown distribution. Consistency of the proposed estimators is proved and their asymptotic properties are presented. Further, adaptive algorithms are employed for tracking possible time-variations in the parameters of the almost periodic signal. Finally, the proposed methods are tested via simulations.<<ETX>>\",\"PeriodicalId\":290798,\"journal\":{\"name\":\"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing\",\"volume\":\"98 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-04-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICASSP.1994.389851\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1994.389851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Extraction of almost periodic signals using cyclostationarity
Extraction of almost periodic signals from their noisy observations is accomplished by exploiting cyclostationarity. The additive noise is allowed to be generally cyclostationary with unknown distribution. Consistency of the proposed estimators is proved and their asymptotic properties are presented. Further, adaptive algorithms are employed for tracking possible time-variations in the parameters of the almost periodic signal. Finally, the proposed methods are tested via simulations.<>