{"title":"双谱雷达目标分类","authors":"Ji Hongbing, Li Jie, Xie Weixin, He Wei","doi":"10.1109/ICOSP.1998.770240","DOIUrl":null,"url":null,"abstract":"With the good performance of higher-order spectra (HOS) techniques for non-Gaussian signal processing and Gaussian noise suppression capability, an AR model parametric bispectrum estimation is presented for conventional radar target return analysis. A reasonable selection of target bispectrum features is made with a formation of target feature vector for target classification. The classification experimental results-on-actual target returns are given, with satisfactory results.","PeriodicalId":145700,"journal":{"name":"ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Bispectrum-based radar target classification\",\"authors\":\"Ji Hongbing, Li Jie, Xie Weixin, He Wei\",\"doi\":\"10.1109/ICOSP.1998.770240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the good performance of higher-order spectra (HOS) techniques for non-Gaussian signal processing and Gaussian noise suppression capability, an AR model parametric bispectrum estimation is presented for conventional radar target return analysis. A reasonable selection of target bispectrum features is made with a formation of target feature vector for target classification. The classification experimental results-on-actual target returns are given, with satisfactory results.\",\"PeriodicalId\":145700,\"journal\":{\"name\":\"ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344)\",\"volume\":\"353 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSP.1998.770240\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSP.1998.770240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the good performance of higher-order spectra (HOS) techniques for non-Gaussian signal processing and Gaussian noise suppression capability, an AR model parametric bispectrum estimation is presented for conventional radar target return analysis. A reasonable selection of target bispectrum features is made with a formation of target feature vector for target classification. The classification experimental results-on-actual target returns are given, with satisfactory results.
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