10th International Congress on Information and Communication Technology

Authors - Indika Udagedara, Brian Helenbrook, Aaron Luttman
Abstract - This paper presents a reduced order modeling (ROM) approach for radiation source identification and localization using data from a limited number of sensors. The proposed ROM method comprises two primary steps: offline and online. In the offline phase, a spatial-energetic basis representing the radiation field for various source compositions and positions is constructed. This is achieved using a stochastic approach based on principal component analysis and maximum likelihood estimation. The online step then leverages these basis functions for determining the complete radiation field from limited data collected from only a few detectors. The parameters are estimated using Bayes rule with a Gaussian prior. The effectiveness of the ROM approach is demonstrated on a simplified model problem using noisy data from a limited number of sensors. The impact of noise on the model’s performance is analyzed, providing insights into its robustness. Furthermore, the approach was extended to real-world radiation detection scenarios, demonstrating that these techniques can be used to localize and identify the energy spectra of mixed radiation sources, composed of several individual sources, from noisy sensor data collected at limited locations.