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Conference papers
Hypothesis tests for the detection of constant speed radiation moving sources
Abstract : As a complement to single and multichannel detection algorithms, inefficient under too low signal-to-noise ratios, temporal correlation algorithms have been introduced to detect radiological material in motion. Test hypothesis methods based on the mean and variance of the signals delivered by the different channels have shown significant gain in terms of a tradeoff between detection sensitivity and false alarm probability. This paper discloses the concept of a new hypothesis test for temporal product detection methods, taking advantage of the Poisson nature of the registered counting signals, and establishes a benchmark between this test and its empirical counterpart. The simulation study validates that in the two relevant configurations of a pedestrian source carrier under respectively high and low count rate radioactive backgrounds, the newly introduced hypothesis test ensures a significantly improved compromise between sensitivity and false alarm, while guaranteeing the stability of its optimization parameter regardless of signal-to-noise ratio variations between 2 to 0.8.
Keywords :
solid scintillation detectors
constant speed radiation moving source detection
single detection algorithm
multichannel detection algorithm
signal-to-noise ratios
temporal correlation algorithms
radiological material detection
hypothesis methods
channel signal variance
detection sensitivity tradeoff
false alarm probability
temporal product detection methods
Poisson nature
registered counting signals
pedestrian source carrier
count rate radioactive backgrounds
optimization parameter
Correlation
Trajectory
Random variables
Benchmark testing
Sensitivity
Detectors
Signal to noise ratio
radiation detection
nuclear materials safeguards
https://hal-cea.archives-ouvertes.fr/cea-01823366
Contributor : Léna Le Roy <>
Submitted on : Tuesday, June 26, 2018 - 8:24:51 AM
Last modification on : Monday, March 30, 2020 - 3:10:51 PM
Contributor : Léna Le Roy <>
Submitted on : Tuesday, June 26, 2018 - 8:24:51 AM
Last modification on : Monday, March 30, 2020 - 3:10:51 PM