{"title":"基于CM-CFAR参数学习的异物碎片雷达平方律检测器","authors":"Kudret Akcapinar, S. Baykut","doi":"10.23919/eumc.2018.8541714","DOIUrl":null,"url":null,"abstract":"One of the main challenges in foreign object debris detection radar systems is the statistical characterization of the received signals. In this paper, Non-Central Chi-square Distribution with Unequal Variances is proposed to characterize the received background signal. A practical search algorithm for the determination of the detection threshold under this distribution is also suggested. Through both Monte Carlo simulations and real data measurements, the comparison of the proposed detection procedure with widely used statistical models is made in terms of fulfillment of the given false alarm probability.","PeriodicalId":171460,"journal":{"name":"2018 15th European Radar Conference (EuRAD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CM-CFAR Parameter Learning Based Square-Law Detector For Foreign Object Debris Radar\",\"authors\":\"Kudret Akcapinar, S. Baykut\",\"doi\":\"10.23919/eumc.2018.8541714\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"One of the main challenges in foreign object debris detection radar systems is the statistical characterization of the received signals. In this paper, Non-Central Chi-square Distribution with Unequal Variances is proposed to characterize the received background signal. A practical search algorithm for the determination of the detection threshold under this distribution is also suggested. Through both Monte Carlo simulations and real data measurements, the comparison of the proposed detection procedure with widely used statistical models is made in terms of fulfillment of the given false alarm probability.\",\"PeriodicalId\":171460,\"journal\":{\"name\":\"2018 15th European Radar Conference (EuRAD)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 15th European Radar Conference (EuRAD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/eumc.2018.8541714\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 15th European Radar Conference (EuRAD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/eumc.2018.8541714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CM-CFAR Parameter Learning Based Square-Law Detector For Foreign Object Debris Radar
One of the main challenges in foreign object debris detection radar systems is the statistical characterization of the received signals. In this paper, Non-Central Chi-square Distribution with Unequal Variances is proposed to characterize the received background signal. A practical search algorithm for the determination of the detection threshold under this distribution is also suggested. Through both Monte Carlo simulations and real data measurements, the comparison of the proposed detection procedure with widely used statistical models is made in terms of fulfillment of the given false alarm probability.