Our NETS solution will apply state-of-the-art neurocomputational methods to partition a trajectory into meaningful segments and then group similar segments into clusters, thus enabling the automatic discovery of common, anomalous, or emergent movement patterns. The initial NETS unsupervised neurocomputational algorithm is able to segment and identify meaningful movement patterns from an aircraft trajectory. The Neurocomputational Model block will be extended to include other data sources such as weather to further develop and refine NETS clustering and anomaly detection capability. In our proposed Phase II effort, we will continue this development but also extend the NETS tool for predicting anomalous movement patterns and enable aircraft trajectory prediction that could include ETA, holding patterns, path stretching, hovering, extreme maneuvering, and non-conformance to nominal patterns. There are two main paths through the NETS architecture: batch processing and real-time streaming analytics. Our proposed NETS tool follows the lambda architecture where there is a batch layer, speed layer and serving layer. The Phase II effort will focus on the batch and speed layer. The batch layer is an extension of our NETS Phase I work where we developed an Aircraft Trajectory Index (ATI) inspired by prior work in Prognostics and Health Management. The ATI is a neuro-representation of an aircraft maneuver computed at each segment of the aircraft trajectory. The length of the segment was heuristically determined and set as a fixed length sequence of latitude, longitude, and altitude. We will develop a multi-resolution approach for computing the ATI allowing for varying granularity providing segment level anomaly prediction and extreme maneuvering detection up to detection and prediction of holding patterns, path stretching, and hovering. That batch layer is focused on history and non-real time clustering, detections, and predictions.
NETS can be useful at launch facilities such as NASA’s Kennedy Space Center and Wallops Flight Facility. Launches are scheduled for a narrow time window, and a precise forecast of air traffic for that time window would determine if a launch is advisable, allowing plans to be updated in advance to minimize airspace disruptions. The ATM-X Project can use NETS for developing UAM/UTM and TBO concepts in the Test Bed modeling and simulation environment.
IAI works with 45th Space Wing on operationalization of IAI’s StarGate Enterprise product. Additionally, we are developing the Cloud and Lightning Evaluation for the Eastern Range, a 3D weather display and alerting capability. We presented future enhancements to 45 SW, which include incorporating air traffic situational awareness and NETS-enabled prognostics for enhanced launch scheduling.