{"title":"相干几何及其在周期平稳时间序列中的应用","authors":"S. Howard, S. Sirianunpiboon, D. Cochran","doi":"10.1109/SSP.2018.8450812","DOIUrl":null,"url":null,"abstract":"The consequences of cyclostationary structure in a random process have traditionally been described in terms of the correlation or coherence of pairs of particular time and frequency shifted versions of the process. However, cyclostationarity, and more generally almost cyclostationarity, are manifest in the mutual coherence of subspaces spanned by sets of time and frequency shifted versions of the process. The generalized coherence framework allows any finite collection of pertinent samples of the cyclic autocorrelation function estimates formed from the measured signal data to be combined into a detection statistic. This paper develops the subspace coherence theory of almost cyclostationary processes as a guide to constructing such detectors in both the time and spectral domains.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"The Geometry of Coherence and Its Application to Cyclostationary Time Series\",\"authors\":\"S. Howard, S. Sirianunpiboon, D. Cochran\",\"doi\":\"10.1109/SSP.2018.8450812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The consequences of cyclostationary structure in a random process have traditionally been described in terms of the correlation or coherence of pairs of particular time and frequency shifted versions of the process. However, cyclostationarity, and more generally almost cyclostationarity, are manifest in the mutual coherence of subspaces spanned by sets of time and frequency shifted versions of the process. The generalized coherence framework allows any finite collection of pertinent samples of the cyclic autocorrelation function estimates formed from the measured signal data to be combined into a detection statistic. This paper develops the subspace coherence theory of almost cyclostationary processes as a guide to constructing such detectors in both the time and spectral domains.\",\"PeriodicalId\":330528,\"journal\":{\"name\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2018.8450812\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Geometry of Coherence and Its Application to Cyclostationary Time Series
The consequences of cyclostationary structure in a random process have traditionally been described in terms of the correlation or coherence of pairs of particular time and frequency shifted versions of the process. However, cyclostationarity, and more generally almost cyclostationarity, are manifest in the mutual coherence of subspaces spanned by sets of time and frequency shifted versions of the process. The generalized coherence framework allows any finite collection of pertinent samples of the cyclic autocorrelation function estimates formed from the measured signal data to be combined into a detection statistic. This paper develops the subspace coherence theory of almost cyclostationary processes as a guide to constructing such detectors in both the time and spectral domains.