Artificial Intelligence Real-Time Track Modeling and Simulation for Combat Systems

Current combat system tracking management algorithms utilize a simple linear predictive model based on last known position and velocity vector to update track data in situations where real-time sensor data is unavailable due to sensor failure or active sensor and/or communications jamming. The linear model becomes a critical point of failure when sensors fail for an extended period of time. A more robust predictive model is required to increase the robustness of track prediction in an environment where the number of potential air and surface targets are significantly increased, as position and velocity vector confidence deteriorates due to interference from additional tracks or under sampling of position measurements.In Phase I, A Real-Time Streaming Analytics Platform (RT-SAP) that enables the identification and path prediction of air targets reliably, quickly, and accurately. A real time framework provides the infrastructure needed to rapidly ingest newly acquired sensor data and distill the information to key metrics to present to a combat system operator. RT-SAP provides the combat system estimated track vector updates based on real-time track simulation and AI techniques utilizing prior track behavior and other data in a communications/sensor compromised environment.