C. Wu, M. Berry, Kayla M. Grieme, S. Sen, N. Rao, R. Brooks, Christopher Temples
{"title":"基于ROSD定位的辐射源网络检测","authors":"C. Wu, M. Berry, Kayla M. Grieme, S. Sen, N. Rao, R. Brooks, Christopher Temples","doi":"10.1109/NSSMIC.2015.7581999","DOIUrl":null,"url":null,"abstract":"Networks of radiation counters are increasingly being deployed in monitoring applications to provide faster and better detection than individual detectors. Their performances critically depend on the algorithms used to aggregate measurements from individual detectors. Recently, localization-based algorithms have been developed for network detection, where multiple source location estimates are generated based on the measurements from various “dispersed” subnets: i) when a source is present, these source location estimates form a single dominant cluster; ii) otherwise, they are spatially dispersed. For example, the triangulation-based detection method [1] employs a closed-form quadratic expression for source location estimates using a subnet of three detectors. This method works well in relatively simple detector configurations, but may exhibit unpredictable performances in complex settings mainly due to the increased number of imaginary roots in the closed-form solution.","PeriodicalId":106811,"journal":{"name":"2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Network detection of radiation sources using ROSD localization\",\"authors\":\"C. Wu, M. Berry, Kayla M. Grieme, S. Sen, N. Rao, R. Brooks, Christopher Temples\",\"doi\":\"10.1109/NSSMIC.2015.7581999\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Networks of radiation counters are increasingly being deployed in monitoring applications to provide faster and better detection than individual detectors. Their performances critically depend on the algorithms used to aggregate measurements from individual detectors. Recently, localization-based algorithms have been developed for network detection, where multiple source location estimates are generated based on the measurements from various “dispersed” subnets: i) when a source is present, these source location estimates form a single dominant cluster; ii) otherwise, they are spatially dispersed. For example, the triangulation-based detection method [1] employs a closed-form quadratic expression for source location estimates using a subnet of three detectors. This method works well in relatively simple detector configurations, but may exhibit unpredictable performances in complex settings mainly due to the increased number of imaginary roots in the closed-form solution.\",\"PeriodicalId\":106811,\"journal\":{\"name\":\"2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NSSMIC.2015.7581999\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSMIC.2015.7581999","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Network detection of radiation sources using ROSD localization
Networks of radiation counters are increasingly being deployed in monitoring applications to provide faster and better detection than individual detectors. Their performances critically depend on the algorithms used to aggregate measurements from individual detectors. Recently, localization-based algorithms have been developed for network detection, where multiple source location estimates are generated based on the measurements from various “dispersed” subnets: i) when a source is present, these source location estimates form a single dominant cluster; ii) otherwise, they are spatially dispersed. For example, the triangulation-based detection method [1] employs a closed-form quadratic expression for source location estimates using a subnet of three detectors. This method works well in relatively simple detector configurations, but may exhibit unpredictable performances in complex settings mainly due to the increased number of imaginary roots in the closed-form solution.