{"title":"A New CFAR Matched Detector for an Autoregressive Model of Noise","authors":"V. Golikov, O. Lebedeva","doi":"10.1109/ICEEE.2006.251911","DOIUrl":null,"url":null,"abstract":"The constant false alarm rate (CFAR) matched detector (CFAR MD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. The CFAR adaptive subspace detector (CFAR MD) was proposed for detecting a target signal in noise whose covariance structure and level are both unknown. In this paper, we use the theory of GLRTs to adapt the no-adaptive CFAR MDs to unknown noise covariance matrices with autoregressive (AR) structure. In this situation, we proposed a new CFAR NCFMD whose structure does not depend on noise covariance matrix and level and its performance penalty is small","PeriodicalId":125310,"journal":{"name":"2006 3rd International Conference on Electrical and Electronics Engineering","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 3rd International Conference on Electrical and Electronics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEE.2006.251911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The constant false alarm rate (CFAR) matched detector (CFAR MD) is the uniformly most-powerful-invariant test and the generalized likelihood ratio test (GLRT) for detecting a target signal in noise whose covariance structure is known but whose level is unknown. The CFAR adaptive subspace detector (CFAR MD) was proposed for detecting a target signal in noise whose covariance structure and level are both unknown. In this paper, we use the theory of GLRTs to adapt the no-adaptive CFAR MDs to unknown noise covariance matrices with autoregressive (AR) structure. In this situation, we proposed a new CFAR NCFMD whose structure does not depend on noise covariance matrix and level and its performance penalty is small