{"title":"光谱等效过程的检测和分类:一种参数化方法","authors":"M. Coulon, J. Tourneret, M. Ghogho","doi":"10.1109/HOST.1997.613557","DOIUrl":null,"url":null,"abstract":"The detection of two spectrally equivalent (SE) processes is addressed. The two SE processes are modeled using two SE parametric models: the noisy AR model and the ARMA model. Higher-order statistics are shown to be an efficient tool for the SE process detection problem. A new detector based on the higher-order Yule-Walker matrix singularity is studied. The detector performance is compared in supervised and unsupervised learning. The model order mismatch is then studied.","PeriodicalId":305928,"journal":{"name":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Detection and classification of spectrally equivalent processes: a parametric approach\",\"authors\":\"M. Coulon, J. Tourneret, M. Ghogho\",\"doi\":\"10.1109/HOST.1997.613557\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The detection of two spectrally equivalent (SE) processes is addressed. The two SE processes are modeled using two SE parametric models: the noisy AR model and the ARMA model. Higher-order statistics are shown to be an efficient tool for the SE process detection problem. A new detector based on the higher-order Yule-Walker matrix singularity is studied. The detector performance is compared in supervised and unsupervised learning. The model order mismatch is then studied.\",\"PeriodicalId\":305928,\"journal\":{\"name\":\"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HOST.1997.613557\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HOST.1997.613557","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection and classification of spectrally equivalent processes: a parametric approach
The detection of two spectrally equivalent (SE) processes is addressed. The two SE processes are modeled using two SE parametric models: the noisy AR model and the ARMA model. Higher-order statistics are shown to be an efficient tool for the SE process detection problem. A new detector based on the higher-order Yule-Walker matrix singularity is studied. The detector performance is compared in supervised and unsupervised learning. The model order mismatch is then studied.
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