Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection

TL;DR

This paper uses detailed simulations to evaluate how different configurations of sparse static detector networks in urban environments can improve the detection of illicit radioactive sources, emphasizing strategic placement and data fusion.

Contribution

It introduces a comprehensive simulation framework to compare static detector network configurations and highlights the importance of data fusion and vehicle attributes in detection performance.

Findings

Higher detector density improves detection probability.

Strategic node placement enhances detection efficiency.

Fusing data from multiple detectors significantly increases detection chances.

Abstract

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs". We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times6$ km$^2$ area of downtown Chicago. A detector network's probability of detecting a source increases with detector density but can also be increased with strategic node placement. We show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this paper we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.