Abstract
Detector dataset in digital data is required to simulate and evaluate a particle filter (PF) algorithm. The PF is used to estimate the position and intensity of unknown hotspots within a region of interest (ROI) from sparse sampling points around the ROI. The dataset will emulate GM measured data at these sampling points. GM radiation detection model, Poisson random variable generator, and Gaussian noise generator were implemented to generate the datasets. Next, using the datasets as input to the PF code, the corresponding output is evaluated for various configuration of hotspot locations and radiation intensities. The resulting detector datasets and the particle filter output were analyzed; and the results are presented in the paper.
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