Background and Anomaly Learning Methods for Static Gamma-Ray Detectors

Bandstra, M. S.; Abgrall, N.; Cooper, R. J.; Hellfeld, D.; Joshi, T. H. Y.; Negut, V.; Quiter, B. J.; Salathe, M.; Sankaran, R.; Kim, Y.; Shahkarami, S.

doi:10.1109/tns.2023.3313996

IEEE Trans. Nucl. Sci.

2023

DOI: 10.1109/tns.2023.3313996

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Background and Anomaly Learning Methods for Static Gamma-Ray Detectors

M. S. Bandstra,

N. Abgrall,

R. J. Cooper

et al.

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2023

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Networked Sensing for Radiation Detection, Localization, and Tracking

Cooper,

Abgrall,

Aversano

et al. 2023

J. Phys.: Conf. Ser.

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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.

show abstract

Networked Sensing for Radiation Detection, Localization, and Tracking

Cooper,

Abgrall,

Aversano

et al. 2023

J. Phys.: Conf. Ser.

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show abstract

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Background and Anomaly Learning Methods for Static Gamma-Ray Detectors

Cited by 1 publication

References 30 publications

Networked Sensing for Radiation Detection, Localization, and Tracking

Networked Sensing for Radiation Detection, Localization, and Tracking

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