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Title: Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks

Abstract

We consider the problem of inferring the operational state of a reactor facility by using measurements from a radiation sensor network, which is deployed around the facility’s ventilation stack. The radiation emissions from the stack decay with distance, and the corresponding measurements are inherently random with parameters determined by radiation intensity levels at the sensor locations. We fuse measurements from network sensors to estimate the intensity at the stack, and use this estimate in a one-sided Sequential Probability Ratio Test (SPRT) to infer the on/off state of the reactor facility. We demonstrate the superior performance of this method over conventional majority vote fusers and individual sensors using (i) test measurements from a network of NaI sensors, and (ii) emulated measurements using radioactive effluents collected at a reactor facility stack. We analytically quantify the performance improvements of individual sensors and their networks with adaptive thresholds over those with fixed ones, by using the packing number of the radiation intensity space.

Authors:
 [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1435192
DOE Contract Number:
AC05-00OR22725
Resource Type:
Book
Country of Publication:
United States
Language:
English

Citation Formats

Ramirez Aviles, Camila A., and Rao, Nageswara S. Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks. United States: N. p., 2018. Web.
Ramirez Aviles, Camila A., & Rao, Nageswara S. Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks. United States.
Ramirez Aviles, Camila A., and Rao, Nageswara S. Mon . "Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks". United States. doi:.
@article{osti_1435192,
title = {Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks},
author = {Ramirez Aviles, Camila A. and Rao, Nageswara S.},
abstractNote = {We consider the problem of inferring the operational state of a reactor facility by using measurements from a radiation sensor network, which is deployed around the facility’s ventilation stack. The radiation emissions from the stack decay with distance, and the corresponding measurements are inherently random with parameters determined by radiation intensity levels at the sensor locations. We fuse measurements from network sensors to estimate the intensity at the stack, and use this estimate in a one-sided Sequential Probability Ratio Test (SPRT) to infer the on/off state of the reactor facility. We demonstrate the superior performance of this method over conventional majority vote fusers and individual sensors using (i) test measurements from a network of NaI sensors, and (ii) emulated measurements using radioactive effluents collected at a reactor facility stack. We analytically quantify the performance improvements of individual sensors and their networks with adaptive thresholds over those with fixed ones, by using the packing number of the radiation intensity space.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2018},
month = {Mon Jan 01 00:00:00 EST 2018}
}

Book:
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