Bayesian Radiation Source Localization

Jarman, Kenneth D.; Miller, Erin A.; Wittman, Richard S.; Gesh, Christopher J.

doi:10.13182/nt10-72

Cited by 27 publications

(17 citation statements)

References 9 publications

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“…In Table 2, attenuation coefficients, μ, determined in this manner from previously measured data are compared with the results obtained from the simulations in this work, adjusted to STP using (2). The linear attenuation coefficients for the K, U, and T windows for the simulations agree with data taken from previous measurements conducted by the IAEA [4] within 3%, 5%, and 2%, respectively.…”

Section: Determination Of Sensitivity Matrixsupporting

confidence: 61%

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Real-time airborne gamma-ray background estimation using NASVD with MLE and radiation transport for calibration

Kulisek

Schweppe

Stave

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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a b s t r a c tHelicopter-mounted gamma-ray detectors can provide law enforcement officials the means to quickly and accurately detect, identify, and locate radiological threats over a wide geographical area. The ability to accurately distinguish radiological threat-generated gamma-ray signatures from background gamma radiation in real time is essential in order to realize this potential. This problem is non-trivial, especially in urban environments for which the background may change very rapidly during flight. This exacerbates the challenge of estimating background due to the poor counting statistics inherent in real-time airborne gamma-ray spectroscopy measurements. To address this challenge, we have developed a new technique for real-time estimation of background gamma radiation from aerial measurements without the need for human analyst intervention. The method can be calibrated using radiation transport simulations along with data from previous flights over areas for which the isotopic composition need not be known. Over the examined measured and simulated data sets, the method generated accurate background estimates even in the presence of a strong, 60 Co source. The potential to track large and abrupt changes in background spectral shape and magnitude was demonstrated. The method can be implemented fairly easily in most modern computing languages and environments.

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Section: Determination Of Sensitivity Matrixsupporting

confidence: 61%

“…Background estimations from the current algorithm will be used as input, to provide a baseline for anomaly detection [1] and localization [2] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Real-time airborne gamma-ray background estimation using NASVD with MLE and radiation transport for calibration

Kulisek

Schweppe

Stave

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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“…Jarman, et al build on the work of Miller and Charlton by applying a probabilistic inverse method to the portal monitoring problem. 54 They solve for the posterior probability distribution for the location and source strength of a point source. They compute the posterior distribution directly at discrete values of each model parameter.…”

Section: Discrete Methodsmentioning

confidence: 99%

Radiation Source Mapping with Bayesian Inverse Methods

Hykes

Azmy

2015

Nuclear Science and Engineering

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HYKES, JOSHUA MICHAEL. Radiation Source Mapping with Bayesian Inverse Methods. (Under the direction of Yousry Y. Azmy.) We present a method to map the spectral and spatial distributions of radioactive sources using a small number of detectors. Locating and identifying radioactive materials is important for border monitoring, accounting for special nuclear material in processing facilities, and in clean-up operations. Most methods to analyze these problems make restrictive assumptions about the distribution of the source. In contrast, the source-mapping method presented here allows an arbitrary three-dimensional distribution in space and a flexible group and gamma peak distribution in energy. To apply the method, the system's geometry and materials must be known. A probabilistic Bayesian approach is used to solve the resulting inverse problem (ip) since the system of equations is ill-posed. The probabilistic approach also provides estimates of the confidence in the final source map prediction. A set of adjoint flux, discrete ordinates solutions, obtained in this work by the Denovo code, are required to efficiently compute detector responses from a candidate source distribution. These adjoint fluxes are then used to form the linear model to map the state space to the response space. The test for the method is simultaneously locating a set of 137 Cs and 60 Co gamma sources in an empty room. This test problem is solved using synthetic measurements generated by a Monte Carlo (m c n p) model and using experimental measurements that we collected for this purpose. With the synthetic data, the predicted source distributions identified the locations of the sources to within tens of centimeters, in a room with an approximately four-by-four meter floor plan. Most of the predicted source intensities were within a factor of ten of their true value. The chi-square value of the predicted source was within a factor of five from the expected value based on the number of measurements employed. With a favorable uniform initial guess, the predicted source map was nearly identical to the true distribution, and the source intensities agreed within the predicted uncertainty. Using experimental data, the mapping was more difficult due to laboratory limitations. However, by supplanting 14 flawed measurements (out of 69 total) with synthetic data, the proof-of-principle source mapping was nearly as accurate as the synthetic-only prediction.

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“…To accomplish this, this work employs Maximum-Likelihood based localization techniques. This sort of approach has been employed to look for radioisotope sources in many search applications, which data was been combined from single or networked radiation detectors to achieve source localization in real time or after the fact [7][8][9][10][11]. Generally, these approaches are based on a formalism where data from each detector or time step is compared with the expected data from every notional source location to determine the most likely location.…”

Section: Maximum Likelihood Localizationmentioning

confidence: 99%

Adaptively Reevaluated Bayesian Localization (ARBL): A novel technique for radiological source localization

Miller

Robinson

Anderson

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

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We present a novel technique for the localization of radiological sources in urban or rural environments from an aerial platform. The technique is based on a Bayesian approach to localization, in which measured count rates in a time series are compared with predicted count rates from a series of pre-calculated test sources to define likelihood. This technique is expanded by using a localized treatment with a limited Field of View (FOV), coupled with a likelihood ratio reevaluation, allowing for real-time computation on commodity hardware for arbitrarily complex detector models and terrain. In particular, detectors with inherent asymmetry of response (such as those employing internal collimation or self-shielding for enhanced directional awareness) are leveraged by this approach to provide improved localization. Results from the localization technique are shown for simulated flight data using monolithic as well as directionally-aware detector models, and the capability of the methodology to locate radioisotopes is estimated for several test cases. This localization technique is shown to facilitate urban search by allowing quick and adaptive estimates of source location, in many cases from a single flyover near a source. In particular, this method represents a significant advancement from earlier methods like full-field Bayesian likelihood, which is not generally fast enough to allow

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Bayesian Radiation Source Localization

Cited by 27 publications

References 9 publications

Real-time airborne gamma-ray background estimation using NASVD with MLE and radiation transport for calibration

Real-time airborne gamma-ray background estimation using NASVD with MLE and radiation transport for calibration

Radiation Source Mapping with Bayesian Inverse Methods

Adaptively Reevaluated Bayesian Localization (ARBL): A novel technique for radiological source localization

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