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Neuro-dynamic programming for adaptive fusion complexity control

The prodigious amount of information provided by surveillance system and other information sources has created unprecedented opportunities for achieving situation awareness. Because the mission's user's needs are constantly evolving, fusion control strategies must adapt to these changing requirements. However, the optimal control problem with the desired adaptive control capabilities is enormously complex. Therefore, we solve the adaptive fusion control problem approximately using a methodology called Neuro- Dynamic Programming (NDP) that combines elements of dynamic programming, simulation-based reinforcement learning, and statistical inference techniques. This work demonstrates the promise of using NDP for adaptive fusion control by sign it to allocate computational resources to Bayesian belief networks that use a variety of data types to track and identify clusters of vehicles. We have significantly extended previous work by using NDP to adapt the fusion process itself in addition to deciding which clusters should get their inference updated. Fusion within the Bayesian networks was adapted by using NDP to select the subset of available data to be used when updating the inference. We also extended previous work by using a dynamic scenario with moving vehicles for training and testing models.