{"title":"谐波信号检测","authors":"J. Piper, S. Reese, W. G. Reese","doi":"10.1109/OCEANS.2012.6404910","DOIUrl":null,"url":null,"abstract":"Harmonic signals are often produced by man-made sources. Their detection has traditionally been accomplished by using Fourier methods. An alternative approach that uses a maximum likelihood function is presented in this paper. The power of this approach comes from modelling the received signal as a family of harmonic signals. This effectively reduces the problem to a one-dimensional search for the fundamental frequency. The coefficients for the various harmonic frequencies can then be found using the Moore-Penrose inverse.","PeriodicalId":434023,"journal":{"name":"2012 Oceans","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Detection of harmonic signals\",\"authors\":\"J. Piper, S. Reese, W. G. Reese\",\"doi\":\"10.1109/OCEANS.2012.6404910\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Harmonic signals are often produced by man-made sources. Their detection has traditionally been accomplished by using Fourier methods. An alternative approach that uses a maximum likelihood function is presented in this paper. The power of this approach comes from modelling the received signal as a family of harmonic signals. This effectively reduces the problem to a one-dimensional search for the fundamental frequency. The coefficients for the various harmonic frequencies can then be found using the Moore-Penrose inverse.\",\"PeriodicalId\":434023,\"journal\":{\"name\":\"2012 Oceans\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Oceans\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OCEANS.2012.6404910\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Oceans","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OCEANS.2012.6404910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Harmonic signals are often produced by man-made sources. Their detection has traditionally been accomplished by using Fourier methods. An alternative approach that uses a maximum likelihood function is presented in this paper. The power of this approach comes from modelling the received signal as a family of harmonic signals. This effectively reduces the problem to a one-dimensional search for the fundamental frequency. The coefficients for the various harmonic frequencies can then be found using the Moore-Penrose inverse.