{"title":"多波形STAP信噪比分析","authors":"S. Blunt, J. Metcalf, John Jakabosky, B. Himed","doi":"10.1109/RADAR.2014.7060341","DOIUrl":null,"url":null,"abstract":"A multi-waveform version of space-time adaptive processing denoted as MuW-STAP (or simply μ-STAP) was recently developed that incorporates the training data generated by secondary waveform/filter pairs into the estimation of the sample covariance matrix. This additional training data was found to improve robustness to heterogeneous clutter. Here SINR analysis is used to evaluate the μ-STAP approach under various clutter conditions and with multiple additional sets of training data obtained through the use of multiple different pulse compression filters applied to the same received data.","PeriodicalId":317910,"journal":{"name":"2014 International Radar Conference","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"SINR analysis of multi-waveform STAP\",\"authors\":\"S. Blunt, J. Metcalf, John Jakabosky, B. Himed\",\"doi\":\"10.1109/RADAR.2014.7060341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A multi-waveform version of space-time adaptive processing denoted as MuW-STAP (or simply μ-STAP) was recently developed that incorporates the training data generated by secondary waveform/filter pairs into the estimation of the sample covariance matrix. This additional training data was found to improve robustness to heterogeneous clutter. Here SINR analysis is used to evaluate the μ-STAP approach under various clutter conditions and with multiple additional sets of training data obtained through the use of multiple different pulse compression filters applied to the same received data.\",\"PeriodicalId\":317910,\"journal\":{\"name\":\"2014 International Radar Conference\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Radar Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RADAR.2014.7060341\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Radar Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR.2014.7060341","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multi-waveform version of space-time adaptive processing denoted as MuW-STAP (or simply μ-STAP) was recently developed that incorporates the training data generated by secondary waveform/filter pairs into the estimation of the sample covariance matrix. This additional training data was found to improve robustness to heterogeneous clutter. Here SINR analysis is used to evaluate the μ-STAP approach under various clutter conditions and with multiple additional sets of training data obtained through the use of multiple different pulse compression filters applied to the same received data.
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