Effects of Background on Gamma-Ray Detection for Mobile Spectroscopy and Imaging Systems

RadMAP: The Radiological Multi-sensor Analysis Platform

“…The gamma-ray imager aboard RadMAP consists of 100 4×4×2 inch NaI(Tl) detectors arranged in a 10×10 configuration, totaling approximately 1 m 2 of the original coded mask pattern that the Naval Research Lab designed for the MISTI vehicle (the "NRL mask") and an improved random mask pattern (the "2015 mask"). Additionally, some data in 2013 were collected with no lead mask pieces in place, and some with a 10×18 version of the 2015 mask, in order to measure the effect of the amount of lead shielding on background variations [8].…”

“…The RadMAP dataset has also been used as a standard for characterizing and assessing the performance of gamma-ray detection algorithms. The dataset has been used to directly compare spectral-and imaging-based detection algorithms [8], evaluate the effects of detector efficiency and resolution using realistic backgrounds [45], and evaluate state-of-the-art detection and identification algorithms [44].…”

“…In [8], data from RadMAP's NaI(Tl) array were analyzed using source injection. Different spectral and imaging algorithms were applied to the data and compared against ideal cases.…”

“…Toward this end, various vehicle-borne, large area detector systems have been designed and tested in recent years [1][2][3][4]. However, the problem of detecting illicit radioactive sources is confounded by the non-Poissonian background fluctuations commonly encountered in operational environments, which greatly limit the capabilities of these systems [5][6][7][8]. These fluctuations are caused by variations in the environment through which the vehicle is moving, but the exact radiological properties and geometric configuration of materials around the vehicle are seldom known well enough to predict or even understand these changes.…”

Fast neutron counting in a mobile, trailer-based search platform

“…These assets include trailer-based sensors [1] and wearable sensors, and they may also be used for intelligence-based search or chokepoint monitoring. Considering trailer-based systems, while non-imaging systems have been demonstrated with the use of advanced algorithms to be highly sensitive for RN detection during search [2][3][4], they are not able to discern on which side of a roadway a source is located. On the other hand, a coded-aperture-based system can perform this task while also sensitively and specifically detecting RN threats [1,[5][6][7][8].…”