Detection and localization of hidden radioactive sources with spatial statistical method

被引:14
|
作者
Wan, Hong [1 ]
Zhang, Tonglin [2 ]
Zhu, Yu [2 ]
机构
[1] Purdue Univ, Dept Ind Engn, W Lafayette, IN 47907 USA
[2] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
Bootstrap; Fisher information; Likelihood ratio statistic; Maximum likelihood estimation; Poisson processes; Signal plus background model; NUISANCE PARAMETER; SENSOR NETWORKS; OPTIMIZATION PROBLEMS; UNIFIED APPROACH; RISK;
D O I
10.1007/s10479-010-0805-z
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
The detection of radioactive materials has become a critical issue for environmental services, public health, and national security. This paper proposes a spatial statistical method to detect and localize a hidden radioactive source. Based on a detection system of multiple radiation detectors, the statistical model assumes that the counts of radiation particles received by those detectors are spatially distributed of Poisson distribution, and each comprises a signal and a background. By considering the physical law of signal degradation with distance, the paper provides a numerical method to compute the maximum likelihood estimates of the strength and location of the source. Based on these estimates, a likelihood ratio statistic is used to test the existence of the source. Because of the special properties of the model, the test statistic does not converge asymptotically to the standard chi-square distribution. Thus a bootstrap method is proposed to compute the p-value in the test. The simulation results show that the proposed method is efficient for detecting and localizing the hidden radioactive source.
引用
收藏
页码:87 / 104
页数:18
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