{"title":"基于动态贝叶斯网络的无人机群被动防空威胁检测与定位","authors":"V. Zotov, Xiaoguang Gao","doi":"10.1109/ICSSEM.2011.6081267","DOIUrl":null,"url":null,"abstract":"This article introduces an algorithm for passive detection and location of air defense threats, based on dynamic Bayesian networks. The algorithm can be applied to mobile robot swarms and uses data on the loss of communication with a UAV for the detection and location of passive enemy air defense threats. The article describes the algorithm and illustrates its work by example.","PeriodicalId":406311,"journal":{"name":"2011 International Conference on System science, Engineering design and Manufacturing informatization","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Passive air defense threat detection and location for UAV swarms based on dynamic Bayesian networks\",\"authors\":\"V. Zotov, Xiaoguang Gao\",\"doi\":\"10.1109/ICSSEM.2011.6081267\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article introduces an algorithm for passive detection and location of air defense threats, based on dynamic Bayesian networks. The algorithm can be applied to mobile robot swarms and uses data on the loss of communication with a UAV for the detection and location of passive enemy air defense threats. The article describes the algorithm and illustrates its work by example.\",\"PeriodicalId\":406311,\"journal\":{\"name\":\"2011 International Conference on System science, Engineering design and Manufacturing informatization\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on System science, Engineering design and Manufacturing informatization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSSEM.2011.6081267\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on System science, Engineering design and Manufacturing informatization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSEM.2011.6081267","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Passive air defense threat detection and location for UAV swarms based on dynamic Bayesian networks
This article introduces an algorithm for passive detection and location of air defense threats, based on dynamic Bayesian networks. The algorithm can be applied to mobile robot swarms and uses data on the loss of communication with a UAV for the detection and location of passive enemy air defense threats. The article describes the algorithm and illustrates its work by example.
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