{"title":"基于模型约简的ARMA谱估计","authors":"B. Wahlberg, B. Ottersten","doi":"10.23919/ACC.1986.4789191","DOIUrl":null,"url":null,"abstract":"In this paper we study how to estimate autoregressive moving average (ARMA) processes via a high order autoregressive (AR) estimate and model reduction. The model reduction techniques considered are based on the L2-norm. internally balanced realizations, or the Hankelnorm. We apply this estimation technique to the problem of finding narrow-band signals in white noise.","PeriodicalId":266163,"journal":{"name":"1986 American Control Conference","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1986-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"ARMA Spectral Estimation via Model Reduction\",\"authors\":\"B. Wahlberg, B. Ottersten\",\"doi\":\"10.23919/ACC.1986.4789191\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we study how to estimate autoregressive moving average (ARMA) processes via a high order autoregressive (AR) estimate and model reduction. The model reduction techniques considered are based on the L2-norm. internally balanced realizations, or the Hankelnorm. We apply this estimation technique to the problem of finding narrow-band signals in white noise.\",\"PeriodicalId\":266163,\"journal\":{\"name\":\"1986 American Control Conference\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1986-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1986 American Control Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ACC.1986.4789191\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1986 American Control Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ACC.1986.4789191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we study how to estimate autoregressive moving average (ARMA) processes via a high order autoregressive (AR) estimate and model reduction. The model reduction techniques considered are based on the L2-norm. internally balanced realizations, or the Hankelnorm. We apply this estimation technique to the problem of finding narrow-band signals in white noise.
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