Aircraft Intent Inference based on Real-Time ADS-B Data Processing

This effort develops Artificial Intelligence (AI)/Machine Learning (ML) capabilities to address a variety of use cases that expand outside the current field of focus of the Department of the Navy (DON). AI/ML algorithms are developed to enable analyses of massive quantities of data in a multitude of applications with a shared focus on program and fleet success.  This effort develops solutions to the following Navy Focus Area: Integration of Automatic Dependent Surveillance – Broadcast (ADS-B) data through AI/ML Applications.  The Navy seeks to develop models and algorithms through AI/ML processes to autonomously characterize behaviors of self-reporting aircraft using ADS-B data. The behavior models and data will be used to (1) identify apparent air corridors and (2) detect anomalous behavior in support of determining aircraft intent. The required processes include pre-mission, mission deployment, and real-time monitoring.  Only during during the pre-mission phase does ML have access to massive quantities of historical data.  The appropriate ML data structures are then passed over to the Navy Application for use in the Mission Deployment phase.  During deployment, a limited amount of adaptation of the ML data structures is possible.  The deployed capability provides timely input to a process where Real-Time Monitoring can apply ML anomalous behavior detection to thereafter invoke AI models for aircraft intent inferences.