{"title":"慢时变幅相周期信号的最大似然频率估计","authors":"A. Logothetis, Syed Khusro Saleem","doi":"10.1109/ISSPA.1996.615743","DOIUrl":null,"url":null,"abstract":"This paper applies the Expectation-Maximisation (EM) algorithm for computing the maximum likelihood (ML) estimate of the fundamental frequency (constant parameter) of a complex multiharmonic signal measured in noise. Most importantly, the individual harmonics have time-varying complex amplitudes, conditional mean estimates of these are obtained using a fixed interval Kalman smoother.","PeriodicalId":359344,"journal":{"name":"Fourth International Symposium on Signal Processing and Its Applications","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Maximum Likelihood Frequency Estimation of Periodic Signals with Slowly Time-Varying Amplitude and Phase\",\"authors\":\"A. Logothetis, Syed Khusro Saleem\",\"doi\":\"10.1109/ISSPA.1996.615743\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper applies the Expectation-Maximisation (EM) algorithm for computing the maximum likelihood (ML) estimate of the fundamental frequency (constant parameter) of a complex multiharmonic signal measured in noise. Most importantly, the individual harmonics have time-varying complex amplitudes, conditional mean estimates of these are obtained using a fixed interval Kalman smoother.\",\"PeriodicalId\":359344,\"journal\":{\"name\":\"Fourth International Symposium on Signal Processing and Its Applications\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fourth International Symposium on Signal Processing and Its Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPA.1996.615743\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth International Symposium on Signal Processing and Its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.1996.615743","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Maximum Likelihood Frequency Estimation of Periodic Signals with Slowly Time-Varying Amplitude and Phase
This paper applies the Expectation-Maximisation (EM) algorithm for computing the maximum likelihood (ML) estimate of the fundamental frequency (constant parameter) of a complex multiharmonic signal measured in noise. Most importantly, the individual harmonics have time-varying complex amplitudes, conditional mean estimates of these are obtained using a fixed interval Kalman smoother.
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