Multiagency Urban Search Experiment Detector and Algorithm Test Bed

Nicholson, Andrew; Garishvili, I.; Peplow, Douglas E.; Archer, D. E.; Ray, William R.; Swinney, Mathew W.; Willis, M.J.; Davidson, Gregory G.; Cleveland, Steven L; Patton, Bradley D; Hornback, D.; Peltz, James J.; McLean, Morgan; Plionis, Alexander A.; Quiter, Brian J.; Bandstra, Mark S.

doi:10.1109/tns.2017.2677092

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…The accuracy of the modeling and detector response generation methodology has been studied using a radiation transport test bed of the Fort Indiantown Gap National Guard facility in Pennsylvania 16,17 . Using measurements at this facility, modeled detector response functions have been compared with experimental data for both background only and source plus background simulations 18 .…”

Section: Technical Validationmentioning

confidence: 99%

Data for training and testing radiation detection algorithms in an urban environment

et al. 2020

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The detection, identification, and localization of illicit nuclear materials in urban environments is of utmost importance for national security. Most often, the process of performing these operations consists of a team of trained individuals equipped with radiation detection devices that have built-in algorithms to alert the user to the presence nuclear material and, if possible, to identify the type of nuclear material present. To encourage the development of new detection, radioisotope identification, and source localization algorithms, a dataset consisting of realistic Monte Carlo–simulated radiation detection data from a 2 in. × 4 in. × 16 in. NaI(Tl) scintillation detector moving through a simulated urban environment based on Knoxville, Tennessee, was developed and made public in the form of a Topcoder competition. The methodology used to create this dataset has been verified using experimental data collected at the Fort Indiantown Gap National Guard facility. Realistic signals from special nuclear material and industrial and medical sources are included in the data for developing and testing algorithms in a dynamic real-world background.

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Section: Technical Validationmentioning

confidence: 99%

Data for training and testing radiation detection algorithms in an urban environment

et al. 2020

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“…The purpose of the Modeling Urban Scenarios and Experiments (MUSE) project [1] is to demonstrate the ability to use modeling and simulation to advance the state of the art in missions related to the search for nuclear materials in urban environments. The project is a joint effort between Oak Ridge National Laboratory (ORNL) and Lawrence Berkeley National Laboratory (LBNL).…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Simulation of Background and Source Measurements with CSG and CAD Geometries

Peplow

Davidson

Celik

et al. 2021

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“…This work is a part of the Modeling Urban Scenarios and Experiments (MUSE) collaboration [28], [29], where the RadMAP vehicle [30] was used to explore the connections between gamma-ray backgrounds and various contextual sensors. Previous work using RadMAP within MUSE consisted of analysis of panoramic imagery at a small mock urban area (the Military Operations in Urban Terrain or MOUT facility in Fort Indiantown Gap, Pennsylvania, or FtIG) [31], for which numerous gamma-ray ground truth measurements had been made [32].…”

mentioning

confidence: 99%

Correlations between Panoramic Imagery and Gamma-Ray Background in an Urban Area

Bandstra

Quiter

Bilton

et al. 2019

2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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When searching for radiological sources in an urban area, a vehicle-borne detector system will often measure complex, varying backgrounds primarily from natural gamma-ray sources. Much work has been focused on developing spectral algorithms that retain sensitivity and minimize the false positive rate even in the presence of such spectral and temporal variability. However, information about the environment surrounding the detector system might also provide useful clues about the expected background, which if incorporated into an algorithm, could improve performance. Recent work has focused on extensive measuring and modeling of urban areas with the goal of understanding how these complex backgrounds arise. This work presents an analysis of panoramic video images and gammaray background data collected in Oakland, California by the Radiological Multi-sensor Analysis Platform (RadMAP) vehicle. Features were extracted from the panoramic images by semantically labeling the images and then convolving the labeled regions with the detector response. A linear model was used to relate the image-derived features to gamma-ray spectral features obtained using Non-negative Matrix Factorization (NMF) under different regularizations. We find some gamma-ray background features correlate strongly with image-derived features that measure the response-adjusted solid angle subtended by sky and buildings, and we discuss the implications for the development of future, contextually-aware detection algorithms. I. INTRODUCTIONV EHICLE-borne gamma-ray detection systems have been developed and deployed for many years and play a key role in radiological and nuclear security missions, especially the search for sources outside of regulatory control [1], [2], [3], [4], [5], [6], [7]. These systems can be rapidly deployed and carry large volume detectors, giving them advantages in efficiency and the ability to cover wide areas relative to humanportable systems. However, with their higher efficiencies, these systems also suffer from the complex natural radiological backgrounds often found in urban areas [8], [9], whose spatial and temporal complexities can be exacerbated by the added

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Multiagency Urban Search Experiment Detector and Algorithm Test Bed

Cited by 11 publications

References 6 publications

Data for training and testing radiation detection algorithms in an urban environment

Data for training and testing radiation detection algorithms in an urban environment

Monte Carlo Simulation of Background and Source Measurements with CSG and CAD Geometries

Correlations between Panoramic Imagery and Gamma-Ray Background in an Urban Area

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