Abstract
In this paper, we develop and numerically illustrate a robust sensor network design to optimally detect a radiation source in an urban environment. This problem exhibits several challenges: penalty functionals are non-smooth due to the presence of buildings, radiation transport models are often computationally expensive, sensor locations are not limited to a discrete number of points, and source intensity and location responses, based on a fixed number of sensors, are not unique. We consider a radiation source located in a prototypical 250 m × 180 m urban setting. To address the non-smooth properties of the model and computationally expensive simulation codes, we employ a verified surrogate model based on radial basis functions. Using this surrogate, we formulate and solve a robust design problem that is optimal in an average sense for detecting source location and intensity with minimized uncertainty.
Highlights
The problem of determining the location and intensity of a radiation source arises in several settings including emergency response to mitigate nuclear threats, structural and nuclear health monitoring in nuclear reactors, and environmental cleanup of biomedical and industrial nuclear waste.In this paper, we consider the development of a robust sensor network design for determining the location and intensity of a radiation source in a simulated urban environment
We summarize in the Appendix A the Particle Swarm algorithm used to solve the inverse problem
We constructed a network of sensors to reduce uncertainty in the solution of a radiation detection inverse problem
Summary
The problem of determining the location and intensity of a radiation source arises in several settings including emergency response to mitigate nuclear threats, structural and nuclear health monitoring in nuclear reactors, and environmental cleanup of biomedical and industrial nuclear waste. We implemented a fast piecewise-continuously differentiable radiation transport model and solved the associated inverse problem using combined global [10,11] and local [12] optimization algorithms, and Bayesian inference techniques [13,14]. To improve computational efficiency and permit gradient-based optimization and the implementation of a robust design algorithm [15], we implement and verify a continuously differentiable surrogate model based on radial basis functions to approximate the response for all possible detector locations. One of the main difficulties associated with optimization of sensor locations is the dependence of the optimal solutions on unknown true values of the source characteristics or prior approximations To remove this dependency, we employ a robust design strategy based on maximizing the expectation of the corresponding local optimality criterion over the source characteristics domain. We summarize in the Appendix A the Particle Swarm algorithm used to solve the inverse problem
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