{"title":"多脉冲子空间探测器","authors":"L. Scharf, Pooria Pakrooh","doi":"10.1109/ACSSC.2017.8335479","DOIUrl":null,"url":null,"abstract":"In this paper we frame a fairly comprehensive set of spacetime detection problems, where a subspace signal modulates the mean-value vector of a multivariate normal measurement and nonstationary additive noise determines the covariance matrix. The measured spacetime data matrix consists of multiple measurements in time. As time advances, the signal component moves around in a subspace and the noise covariance matrix changes in scale.","PeriodicalId":296208,"journal":{"name":"2017 51st Asilomar Conference on Signals, Systems, and Computers","volume":"70 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Multipulse subspace detectors\",\"authors\":\"L. Scharf, Pooria Pakrooh\",\"doi\":\"10.1109/ACSSC.2017.8335479\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we frame a fairly comprehensive set of spacetime detection problems, where a subspace signal modulates the mean-value vector of a multivariate normal measurement and nonstationary additive noise determines the covariance matrix. The measured spacetime data matrix consists of multiple measurements in time. As time advances, the signal component moves around in a subspace and the noise covariance matrix changes in scale.\",\"PeriodicalId\":296208,\"journal\":{\"name\":\"2017 51st Asilomar Conference on Signals, Systems, and Computers\",\"volume\":\"70 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 51st Asilomar Conference on Signals, Systems, and Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACSSC.2017.8335479\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 51st Asilomar Conference on Signals, Systems, and Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACSSC.2017.8335479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper we frame a fairly comprehensive set of spacetime detection problems, where a subspace signal modulates the mean-value vector of a multivariate normal measurement and nonstationary additive noise determines the covariance matrix. The measured spacetime data matrix consists of multiple measurements in time. As time advances, the signal component moves around in a subspace and the noise covariance matrix changes in scale.
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