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In multispectral and hyperspectral sensing, there is a growing need for a versatile sensor capable of adapting response and improving detection of hard-to-find dynamic targets of interest in contested environments. Such on-the-fly adaptivity in current systems requires significant data resources and computation time for data analysis. In order to implement practical systems with this capability, sensors...
We report a RF and IR data-integration strategy based on a probabilistic (or a distribution) model. At the heart of our approach is the ability to extract the probability density functions (pdfs) from the sensed dataset for RF and IR respectively followed by the detection or target identification process based on posterior fusion (i.e., the product of individual pdfs) and Bayesian decision process...
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