Multispectral target recognition using adaptive radar and infrared data integration

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. The pdf-acquisition processes in RF and IR modules have been further refined with clutter models and data-compression techniques.