A Hierarchical Collective Agents Network for Real-Time Sensor Fusion and Decision Support

Plamen V. Petrov, Qiuming Zhu, Jeffrey D. Hicks, and Alexander D. Stoyen

This research addresses a problem of how to make effective use of real-time information acquired from multiple sensor and heterogeneous data resources, and reasoning on the gathered information for situation assessment and impact assessment (SA/IA), thus to provide reliable decision support for time-critical operations. A hierarchical collective agents network (HCAN) is employed as a solution to this problem. The agents network supports multi-sensor registration, real-time sensor/platform cueing, level-2 and level-3 information fusion, and has an arm toward the level-4 fusion objectives. An agent component assembly and decision-support-system-development environment, the 21 Century systems’ AEDGETM software package, is used for the design and implementation of a HCAN-DSS system.

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