{"title":"频谱估计的相关矩阵估计和阶数选择","authors":"W. Du, R. Kirlin","doi":"10.1109/SSAP.1992.246854","DOIUrl":null,"url":null,"abstract":"This paper presents a novel covariance matrix estimator for frequency estimation in time sequence analysis. A preliminary covariance matrix of size M' is first calculated by the sample covariance matrix method, and then the final covariance of size M, with M<or=M' is determined by employing all available correlation information in the preliminary estimate. Generally the new covariance estimator can more effectively utilize temporal correlations among the data and provides more trade-off freedom in order selection. When the orders (sizes) of the covariance matrices are properly selected, this new estimator can obtain a statistically more stable estimate of covariance matrix than the conventional approach.<<ETX>>","PeriodicalId":309407,"journal":{"name":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Correlation matrix estimation and order selection for spectrum estimation\",\"authors\":\"W. Du, R. Kirlin\",\"doi\":\"10.1109/SSAP.1992.246854\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel covariance matrix estimator for frequency estimation in time sequence analysis. A preliminary covariance matrix of size M' is first calculated by the sample covariance matrix method, and then the final covariance of size M, with M<or=M' is determined by employing all available correlation information in the preliminary estimate. Generally the new covariance estimator can more effectively utilize temporal correlations among the data and provides more trade-off freedom in order selection. When the orders (sizes) of the covariance matrices are properly selected, this new estimator can obtain a statistically more stable estimate of covariance matrix than the conventional approach.<<ETX>>\",\"PeriodicalId\":309407,\"journal\":{\"name\":\"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing\",\"volume\":\"102 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSAP.1992.246854\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1992] IEEE Sixth SP Workshop on Statistical Signal and Array Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSAP.1992.246854","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Correlation matrix estimation and order selection for spectrum estimation
This paper presents a novel covariance matrix estimator for frequency estimation in time sequence analysis. A preliminary covariance matrix of size M' is first calculated by the sample covariance matrix method, and then the final covariance of size M, with M>