Hypothesis-driven distributed sensor management

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Abstract
The primary thrusts of the intelligent multisource, multisensor integration (IMMSI) effort are to formalize an approach to hypothesis-driven distributed sensor management, validate that approach, identify candidates for decision support, and investigate implementations of appropriate cognitive processing modules. Using the existing manual voice communication-based cooperative process as a model, a coherent suite of human-machine interfaces, data communication protocols, and decision aids are being developed with the goal of real-time global optimal sensor allocation within the mission context. The Knowledgeable Observer And Linked Advice System (KOALAS) architecture provides a framework for constructing the operator-inductive/machine- deductive IMMSI system. The machine continuously updates a model of the environment from both local and remote sensor data. The operator interacts with the system by evaluating the perceived model and tuning it through the introduction of hypotheses. These hypotheses, also shared among platforms, provide cues for sensor management. The evolving sensor allocation provides new data for the model and a closed-loop intelligent control system is created. The cooperative agent paradigm provides a cognitive model for the IMMSI distributed sensor management process. In a typical cooperative task the common goal is achieved by the agents performing discrete transactions on a shared system state vector. Within the tactical environment, however, centralization of data is neither desirable nor possible; hence, coherency of a distributed track, hypothesis, and global sensor allocation database is also an issue.

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