Data synthesis improves detection of radiation sources in urban environments

Abstract

Radiation detectors in and around airports and other ports of entry play a crucial role in preventing nuclear terrorism. While these systems aim to detect and intercept nuclear materials before they enter a country, they are not foolproof. To address this, distributed sensors have been proposed to detect nuclear materials if they enter urban areas. Past work on small detector systems has shown that data fusion can improve detection. Here, we show how this could be done for a large detector network using Pearson's Method. We evaluate how a sensor network would perform in New York City using a combination of radiation transport and geographic information systems. We use OpenStreetMap data to construct a grid over the streets and analyze vehicle paths using pickup and drop off data from the New York State Department of Transportation. The results show that data synthesis in a large network (40 k detectors) not only improves the time to the first detection but reduces the number of missed sources by up to 99 %.