{"title":"Parameter estimation using the autocorrelation of the discrete Fourier transform","authors":"M. Manry, C. T. Huddleston","doi":"10.1109/ICASSP.1987.1169901","DOIUrl":null,"url":null,"abstract":"Optimal parameter estimation algorithms are developed using the maximum likelihood technique, when no statistics are available for the parameter. Sub-optimal parameter estimates, using one sample of the autocorrelation of the DFT, have been developed previously. In this paper, maximum likelihood estimates are derived, given the auto-correlation function of the received signal's DFT. These estimates sometimes require less computation time than conventional estimates, and frequently have a closed form or simple iterative implementation.","PeriodicalId":140810,"journal":{"name":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1987-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1987.1169901","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
Optimal parameter estimation algorithms are developed using the maximum likelihood technique, when no statistics are available for the parameter. Sub-optimal parameter estimates, using one sample of the autocorrelation of the DFT, have been developed previously. In this paper, maximum likelihood estimates are derived, given the auto-correlation function of the received signal's DFT. These estimates sometimes require less computation time than conventional estimates, and frequently have a closed form or simple iterative implementation.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
