Space systems are an important part of everyday life. They provide global positioning data, communications, and Earth science data such as weather information. All space systems require satellite operators to ensure high performance and continuous operations in the presence of off-nominal conditions due to space weather and onboard anomalies. Similar to other high-stress, time critical operations (e.g., piloting an aircraft or operating a nuclear power plant), situation awareness is a crucial factor in operator performance during these conditions. Because situation awareness is largely acquired by monitoring large numbers of parameters, it is difficult to rapidly and accurately fuse the data to develop an accurate assessment. To aid operators in this task, we have developed a prototype Multi-Agent Satellite System for Information Fusion (MASSIF) for automated data fusion and situation awareness. This system is based on human cognitive decision-making models and integrates a fuzzy logic system for semantic data processing, Bayesian belief networks for multi-source data fusion and situation assessment, and rule-bases for automatic network construction. This paper describes initial simulation-based results to establish feasibility and baseline performance. We describe knowledge engineering efforts, belief network construction, and operator-interfaces for automated data fusion and situation awareness for a hypothetical geosynchronous satellite.

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