{"title":"无源雷达机会选择照明器","authors":"Yang Li, Qian He, Rick S. Blum","doi":"10.1109/CAMSAP.2017.8313066","DOIUrl":null,"url":null,"abstract":"Passive radar can obtain performance benefits by using multiple receivers and multiple illuminators of opportunity (IOO). However, employing a large number of IOOs is costly in terms of hardware. Thus, it is sometimes necessary to limit the total number of selected IOOs. The IOO selection scheme for maximizing target detection performance is studied in this paper, under the assumption that the number of IOOs that can be selected at each receiver is limited. An IOO selection algorithm based on maximizing the Kullback-Leibler (KL) distance is presented, which requires much lower computational complexity compared with the exhaustive search method and is shown to lead to a detection performance that is close enough to that of the optimal selection.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Illuminator of opportunity selection for passive radar\",\"authors\":\"Yang Li, Qian He, Rick S. Blum\",\"doi\":\"10.1109/CAMSAP.2017.8313066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Passive radar can obtain performance benefits by using multiple receivers and multiple illuminators of opportunity (IOO). However, employing a large number of IOOs is costly in terms of hardware. Thus, it is sometimes necessary to limit the total number of selected IOOs. The IOO selection scheme for maximizing target detection performance is studied in this paper, under the assumption that the number of IOOs that can be selected at each receiver is limited. An IOO selection algorithm based on maximizing the Kullback-Leibler (KL) distance is presented, which requires much lower computational complexity compared with the exhaustive search method and is shown to lead to a detection performance that is close enough to that of the optimal selection.\",\"PeriodicalId\":315977,\"journal\":{\"name\":\"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CAMSAP.2017.8313066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CAMSAP.2017.8313066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Illuminator of opportunity selection for passive radar
Passive radar can obtain performance benefits by using multiple receivers and multiple illuminators of opportunity (IOO). However, employing a large number of IOOs is costly in terms of hardware. Thus, it is sometimes necessary to limit the total number of selected IOOs. The IOO selection scheme for maximizing target detection performance is studied in this paper, under the assumption that the number of IOOs that can be selected at each receiver is limited. An IOO selection algorithm based on maximizing the Kullback-Leibler (KL) distance is presented, which requires much lower computational complexity compared with the exhaustive search method and is shown to lead to a detection performance that is close enough to that of the optimal selection.