Experimental verification of algorithms for detection and estimation of radioactive sources

A. Gunatilaka, B. Ristic, M. Morelande
{"title":"Experimental verification of algorithms for detection and estimation of radioactive sources","authors":"A. Gunatilaka, B. Ristic, M. Morelande","doi":"10.1109/ICIF.2010.5711880","DOIUrl":null,"url":null,"abstract":"The paper considers the problem of estimating the number of radioactive point sources that potentially exist in a designated area and estimating the parameters of these sources (their locations and strengths) using measurements collected by a low-cost Geiger-Müller counter. In a recent publication the authors proposed candidate algorithms for this task: the maximum likelihood estimator (MLE) and the importance sampling estimator based on progressive correction (PC) for source parameter estimation, and the minimum description length (MDL) for the estimation of the number of sources. Using real experimental data acquired during a recent radiological field trial in Pucka-punyal Military Area (Victoria, Australia), in the presence of up to three point sources of gamma radiation, this paper presents an experimental verification of the measurement model and algorithms proposed by us earlier. These experimental results show that while the MLE performs well when no more than two sources are present, the PC performs remarkably well for all data sets, which confirms our previous conclusions based on simulation studies alone.","PeriodicalId":341446,"journal":{"name":"2010 13th International Conference on Information Fusion","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 13th International Conference on Information Fusion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIF.2010.5711880","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

The paper considers the problem of estimating the number of radioactive point sources that potentially exist in a designated area and estimating the parameters of these sources (their locations and strengths) using measurements collected by a low-cost Geiger-Müller counter. In a recent publication the authors proposed candidate algorithms for this task: the maximum likelihood estimator (MLE) and the importance sampling estimator based on progressive correction (PC) for source parameter estimation, and the minimum description length (MDL) for the estimation of the number of sources. Using real experimental data acquired during a recent radiological field trial in Pucka-punyal Military Area (Victoria, Australia), in the presence of up to three point sources of gamma radiation, this paper presents an experimental verification of the measurement model and algorithms proposed by us earlier. These experimental results show that while the MLE performs well when no more than two sources are present, the PC performs remarkably well for all data sets, which confirms our previous conclusions based on simulation studies alone.
放射源检测和估计算法的实验验证
本文考虑了利用低成本盖格-迈勒计数器收集的测量数据估计指定区域内潜在存在的放射性点源的数量和估计这些源的参数(它们的位置和强度)的问题。在最近的一篇论文中,作者提出了该任务的候选算法:用于源参数估计的最大似然估计器(MLE)和基于渐进校正的重要抽样估计器(PC),以及用于估计源数量的最小描述长度(MDL)。本文利用最近在Pucka-punyal军区(澳大利亚维多利亚州)进行的一次辐射野外试验中获得的真实实验数据,对我们之前提出的测量模型和算法进行了实验验证。这些实验结果表明,虽然MLE在不超过两个源存在时表现良好,但PC在所有数据集上表现非常好,这证实了我们之前仅基于模拟研究的结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信