Metron proposes develop a neural network to monitor aircraft to detect and inform air traffic control (ATC) of potential safety issues in time for corrective actions to be taken before serious problems develop. Our approach leverages our familiarity with the relevant data and extends our prototype neural network that has demonstrated the ability to learn the typical flight dynamics of aircraft arriving at JFK.
We will address the technical questions of how to monitor the NAS continuously—using data fused from potentially different sources—and automatically identify anomalous flights that indicate safety hazards or precursors to such, while reducing the false alarm rate of such a system. Additionally, we will establish the feasibility of extending the model to predict the probabilities of the occurrence of hazardous events and of their safety risks. Such a system will make predictions “in time” for ATC to take corrective action to minimize the effect of such events.
Metron has teamed with ATAC who will provide subject matter expertise to visualize and assess the safety hazards identified by the system.
Our flight anomaly detection and risk prediction software will provide a key capability for NASA’s In-time System-wide Safety Assurance (ISSA) initiative. This software may be integrated into the Air Traffic Management – eXploration (ATM-X) test bed for real-time evaluation and external validation, before an eventual operational transition via NASA’s participation in the FAA NextGen program.
Terminal ATC users can be alerted both to flights exhibiting anomalous behavior and to predictions of future risks. Beyond this, our technology can be adapted to new domains for Metron’s DoD clientele. For the Navy, this technology can improve motion models used to associate detections to tracks and/or identify anomalous commercial shipping behaviors for the Navy’s Maritime Domain Awareness.