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Detection and localization of hidden radioactive sources with spatial statistical method

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Abstract

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.

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References

  • Andrews, D. W., & Ploberger, W. (1995). Admissibility of the likelihood ratio test when a nuisance parameter is present only under the alternative. Annals of Statistics, 23, 1609–1629.

    Article  Google Scholar 

  • Ausiello, G., D’Atri, A., & Protasi, M. (1980a). Structure preserving reductions among convex optimization problems. Journal of Computer and System Sciences, 21, 136–153.

    Article  Google Scholar 

  • Ausiello, G., Marchetti-Spaccamela, A., & Protasi, M. (1980b). Toward a unified approach for the classification of NP-complete optimization problems. Theoretical Computer Science, 12, 83–96.

    Article  Google Scholar 

  • Brennan, S. M., Maccabe, A. B., Mielke, A. M., & Torney, D. C. (2004). Radiation detection with distributed sensor networks. IEEE Computer, 37(8), 57–59.

    Article  Google Scholar 

  • Chong, C. Y., & Kumar, S. P. (2003). Sensor networks: evaluation, opportunities and challenges. Proceedings of the IEEE, 91(8), 1247–1256.

    Article  Google Scholar 

  • Davies, R. (1977). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 64, 247–254.

    Article  Google Scholar 

  • Davies, R. (1987). Hypothesis testing when a nuisance parameter is present only under the alternatives. Biometrika, 74, 33–43.

    Google Scholar 

  • Dersch, R. (2004). Primary and secondary measurements of 222Rn. Applied Radiation and Isotopes, 60, 387–390.

    Article  Google Scholar 

  • Diggle, P., Morris, S., Elliott, P., & Shaddick, G. (1997). Regression modeling of disease in relation to point sources. Journal of Royal Statistical Society A, 160, 491–505.

    Article  Google Scholar 

  • Drew, C. H., Grace, D. A., Silbernagel, S. M., Hemmings, E. S., Smith, A., Griffith, W. C., Takaro, T. K., & Faustman, E. M. (2003). Nuclear waste transportation: case studies of identifying stakeholder risk information needs. Environmental Health Perspective, 111, 263–272.

    Article  Google Scholar 

  • Efron, B. (1979). Bootstrap methods: another look at the jackknife. Annals of Statistics, 7, 1–26.

    Article  Google Scholar 

  • Fetter, S., Frolov, V. A., Miller, M., Mozley, R., Prilutsky, O. F., Rodionov, S. N., & Sagdeev, R. Z. (1990). Detection nuclear warheads. Science and Global Security, 1, 225–302.

    Article  Google Scholar 

  • Fetter, S., & Cochran, T. B. (1990). Gamma ray measurements of a Soviet cruise missile warhead. Science, 248, 828–834.

    Article  Google Scholar 

  • Feldman, G. J., & Cousins, R. (1998). Unified approach to the classical statistical analysis of small signals. Physical Review D, 57, 3873–3889.

    Article  Google Scholar 

  • Gawande, K., & Smith, H. (2001). Nuclear waste transport and residential property values: estimating the effects of perceived risks. Journal of Environmental Economics and Management, 42, 207–233.

    Article  Google Scholar 

  • Katenka, N., Levina, E., & Michailidis, G. (2008). Local vote decision fusion for target detection in wireless sensor networks. IEEE Transactions on Signal Processing, 56, 329–338.

    Article  Google Scholar 

  • Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics Theory and Methods, 26, 1481–1496.

    Article  Google Scholar 

  • Lehmann, E. L. (1986). Testing statistical hypotheses (2nd edn.). New York: Wiley.

    Google Scholar 

  • Press, W., Teukolsky, S. A., Vetterling, W., & Flannery, B. P. (1992). Numerical recipes in C. Cambridge: Cambridge University Press.

    Google Scholar 

  • Roe, B., & Woodroofe, M. (1999). Improved probability method for estimating signal in the presence of background. Physical Reviews D, 60(55), 053009.

    Article  Google Scholar 

  • van der Vaart, A. W. (1998). Asymptotic statistics. Cambridge: Cambridge University Press.

    Google Scholar 

  • Zhang, T., & Woodroofe, M. (2003). Credible and confidence sets for restricted parameter spaces. Journal of Statistical Planning and Inference, 115, 479–490.

    Article  Google Scholar 

  • Zhang, T. (2006). Existence of the signal in the signal plus background model. IMS Lecture Notes-Monograph Series, 50, 144–155.

    Article  Google Scholar 

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Correspondence to Hong Wan.

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Wan, H., Zhang, T. & Zhu, Y. Detection and localization of hidden radioactive sources with spatial statistical method. Ann Oper Res 192, 87–104 (2012). https://doi.org/10.1007/s10479-010-0805-z

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  • DOI: https://doi.org/10.1007/s10479-010-0805-z

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