Autonomous Situation Awareness Through Threat Data Integration

The ability to dynamically collect and analyze threat data and to accurately report the current battlefield situation is critical in the face of emergent hostile attacks, and enables battlefield helicopters to continually function despite of potential threats. The paper is to model threats to battlefield helicopters, which represents a specific threat pattern and a methodology that compiles the threat into a set of rules using machine learning algorithms. This methodology based upon the inductive threat model can be used to detect real-time threats. We report experimental results that demonstrate the distinctive and predictive patterns of threats in simulated battlefield settings, and show the potential of compilation methods for the successful detection of threat systems.