{"title":"基于噪声协方差数据的Arma极点零估计的一种新的Hankel近似方法*","authors":"S. Kung, K. S. Arun","doi":"10.1364/srs.1983.wa19","DOIUrl":null,"url":null,"abstract":"Model based methods have been gaining popularity in high resolution spectral estimation, and have recently demonstrated a great deal of success. Such methods allow us to parameterize the spectrum in terms of a relatively small number of unknown parameters, and thus reduce the spectral estimation problem to that of first, selecting the appropriate model, and second, estimating its parameters. The most popular models used today, are\n 1) Autoregressive model (AR),\n 2) Sinusoids plus noise model (S+N) and\n 3) Autoregressive moving average model (ARMA)","PeriodicalId":279385,"journal":{"name":"Topical Meeting on Signal Recovery and Synthesis with Incomplete Information and Partial Constraints","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1983-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Novel Hankel Approximation Method for Arma Pole-Zero Estimation from Noisy Covariance Data*\",\"authors\":\"S. Kung, K. S. Arun\",\"doi\":\"10.1364/srs.1983.wa19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Model based methods have been gaining popularity in high resolution spectral estimation, and have recently demonstrated a great deal of success. Such methods allow us to parameterize the spectrum in terms of a relatively small number of unknown parameters, and thus reduce the spectral estimation problem to that of first, selecting the appropriate model, and second, estimating its parameters. The most popular models used today, are\\n 1) Autoregressive model (AR),\\n 2) Sinusoids plus noise model (S+N) and\\n 3) Autoregressive moving average model (ARMA)\",\"PeriodicalId\":279385,\"journal\":{\"name\":\"Topical Meeting on Signal Recovery and Synthesis with Incomplete Information and Partial Constraints\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1983-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Topical Meeting on Signal Recovery and Synthesis with Incomplete Information and Partial Constraints\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/srs.1983.wa19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topical Meeting on Signal Recovery and Synthesis with Incomplete Information and Partial Constraints","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/srs.1983.wa19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Novel Hankel Approximation Method for Arma Pole-Zero Estimation from Noisy Covariance Data*
Model based methods have been gaining popularity in high resolution spectral estimation, and have recently demonstrated a great deal of success. Such methods allow us to parameterize the spectrum in terms of a relatively small number of unknown parameters, and thus reduce the spectral estimation problem to that of first, selecting the appropriate model, and second, estimating its parameters. The most popular models used today, are
1) Autoregressive model (AR),
2) Sinusoids plus noise model (S+N) and
3) Autoregressive moving average model (ARMA)