Propagation of Uncertainty in Anticipatory Image Exploitation Using Polynomial Chaos Random Process Representations

The ability to accurately anticipate target behavior on the basis of surveillance data is critical in many military and civilian contexts. Information regarding target behavior may be drawn from a variety of sources, each of which suffers from uncertainties in the form of noise, inaccuracies, and outright errors. This proposal seeks to develop novel methods for dealing with this uncertainty by vertically integrating uncertainty models in a common framework through all levels of data processing, by adapting uncertainty models over time to incorporate newly observed behaviors and interactions, and by leveraging powerful new adaptive processing techniques. The resulting technology will propagate uncertainties from inputs and models, producing a distribution over anticipated behaviors and a characterization of the most likely future target tracks and associated likelihood measures. This output can be used to intelligently manage sensor and targeting assets, to minimize the need for a human operator to supervise system operation, and to quickly detect targets that deviate from predicted behavior.