Networked Sensing for Radiation Detection, Localization, and Tracking

TL;DR

This paper presents a city-scale multi-sensor network testbed that enhances radiological threat detection, localization, and tracking by integrating radiation sensors with contextual data like video and meteorological sensors.

Contribution

It introduces novel techniques for adaptive, city-scale source detection, localization, and tracking using integrated multi-sensor networks in urban environments.

Findings

Effective multi-sensor fusion improves detection accuracy.

City-scale network enables continuous R/N surveillance.

Enhanced situational awareness through sensor integration.

Abstract

The detection, identification, and localization of illicit radiological and nuclear material continue to be key components of nuclear non-proliferation and nuclear security efforts around the world. Networks of radiation detectors deployed at strategic locations in urban environments have the potential to provide continuous radiological/nuclear (R/N) surveillance and provide high probabilities of intercepting threat sources. The integration of contextual information from sensors such as video, Lidar, and meteorological sensors can provide significantly enhanced situational awareness, and improved detection and localization performance through the fusion of the radiological and contextual data. In this work, we present details of our work to establish a city-scale multi-sensor network testbed for intelligent, adaptive R/N detection in urban environments, and develop new techniques that enable city-scale source detection, localization, and tracking.