{"title":"广义预测器的优化","authors":"K. Koppinen, J. Yli-Hietanen, U. Astola","doi":"10.1109/IMTC.1997.603916","DOIUrl":null,"url":null,"abstract":"Polynomial FIR predictors are generalized to include also the prediction of sinusoidal signals. A general optimization method is presented for optimizing these predictors, which gives optimal FIR predictors with respect to the noise gain, the autocorrelation matrix of additive noise and frequency response as special cases.","PeriodicalId":124893,"journal":{"name":"IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Optimization of generalized predictors\",\"authors\":\"K. Koppinen, J. Yli-Hietanen, U. Astola\",\"doi\":\"10.1109/IMTC.1997.603916\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Polynomial FIR predictors are generalized to include also the prediction of sinusoidal signals. A general optimization method is presented for optimizing these predictors, which gives optimal FIR predictors with respect to the noise gain, the autocorrelation matrix of additive noise and frequency response as special cases.\",\"PeriodicalId\":124893,\"journal\":{\"name\":\"IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-05-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IMTC.1997.603916\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IMTC.1997.603916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Polynomial FIR predictors are generalized to include also the prediction of sinusoidal signals. A general optimization method is presented for optimizing these predictors, which gives optimal FIR predictors with respect to the noise gain, the autocorrelation matrix of additive noise and frequency response as special cases.