{"title":"雷达探测与预分类的多假设检验方法","authors":"M. Greco, F. Gini, A. Farina","doi":"10.1109/NRC.2004.1316469","DOIUrl":null,"url":null,"abstract":"This work presents a single-scan-processing approach to the problem of detecting and pre-classifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their covariance matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probabilities of false alarm, detection and classification. The extra signal-to-noise power ratio necessary to preclassify a target once a detection has occurred is also derived.","PeriodicalId":268965,"journal":{"name":"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2004-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A multiple hypotheses testing approach to radar detection and pre-classification\",\"authors\":\"M. Greco, F. Gini, A. Farina\",\"doi\":\"10.1109/NRC.2004.1316469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work presents a single-scan-processing approach to the problem of detecting and pre-classifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their covariance matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probabilities of false alarm, detection and classification. The extra signal-to-noise power ratio necessary to preclassify a target once a detection has occurred is also derived.\",\"PeriodicalId\":268965,\"journal\":{\"name\":\"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NRC.2004.1316469\",\"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 2004 IEEE Radar Conference (IEEE Cat. No.04CH37509)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NRC.2004.1316469","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multiple hypotheses testing approach to radar detection and pre-classification
This work presents a single-scan-processing approach to the problem of detecting and pre-classifying a radar target that may belong to different target classes. The proposed method is based on a hybrid of the maximum a posteriori (MAP) and Neyman-Pearson (NP) criteria and guarantees the desired constant false alarm rate (CFAR) behavior. The targets are modeled as subspace random signals having zero mean and given covariance matrix. Different target classes are discriminated based on their different signal subspaces, which are specified by their covariance matrices. Performance is investigated by means of numerical analysis and Monte Carlo simulation in terms of probabilities of false alarm, detection and classification. The extra signal-to-noise power ratio necessary to preclassify a target once a detection has occurred is also derived.
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