Abstract:
A system intended to detect, classify, and track radioactive sources in moving
vehicle traffic is under development at Lawrence Livermore National Laboratory
(LLNL). This system will fuse information from a network of sensor suites to provide
real time tracking of the location of vehicles emitting gamma and/or neutron radiation.
This work examined aspects of the source terms of interest and applicable gamma
detection technologies for passive detection of emitted gamma radiation. The severe
restriction placed on the length of count due to motion of the source is presented.
Legitimate gamma sources expected in traffic are discussed. The requirement to
accurately classify and discriminate against these "nuisance" sources and cost restraints
dictate the choice of NaI(Tl) detectors for this application. The development of a
capability to automatically analyze short duration, low signal-to-noise NaI(Tl) spectra
collected from vehicles passing a large, stationary detector is reported. The analysis
must reliably discriminate between sources commonly transported in motor vehicles
and alert on the presence of sources of interest. A library of NaI(Tl) spectra for 33
gamma emitting sources was generated with MCNP4B Monte Carlo modeling. These
simulated spectra were used as parent distributions, from which multiple realizations of
short duration spectra were sampled. Principal component analysis (PCA) of this data
set yielded eigenvectors that enable the conversion of unknown spectra into principal
component space (PCS). An algorithm using least squares fitting of the positions of
library sources in PCS as basis functions, capable of identifying library nuclides in
unidentified spectra, is reported. Analysis results for experimental spectra are compared
against those achieved using simulated spectra. A valuable characteristic of this method
is its ability to identify sources despite unknown shielding geometries. The successful
identification of radionuclides and false identification rates found were excellent for the
signal levels involved. For many of the sources, identification performance against
experimental spectra was somewhat poorer than found using simulated spectra. The
results demonstrate that the PCA-based algorithm is capable of high success rates in
identifying sources in short duration, low signal-to-noise NaI(Tl) spectra.
