Analysis of Automatic Dependent Surveillance-Broadcast Data

Deep Models and Artificial Intelligence for Military Applications: AAAI Technical Report FS-17-03The primary research objective is to detect commercial flight baselines, anomalies and patterns using Automatic Dependent Surveillance – Broadcast (ADS-B) data to analyze aircraft tracks over a period time using Lexical Link Analysis (LLA) and visualizing with Google Earth and Maps. This research could potentially improve situational awareness for naval air warfare decision makers. LLA is a form of text mining showing relationships and associations with the given data. Because there is a large amount of daily ADS-B data, a Hadoop cluster is utilized for parallel processing and then LLA provides data visualizations, patterns and associations for profiling the aircraft based on the kinematic characteristics. Based on the correlation to the speed and altitude of aircraft and its country of origin, our results did identify unusual behavior for some aircrafts. When the kinematic and behavior patterns are discovered from historical ADS-B data, the resulted models can also be used to identify flying patterns and anomalies that can increase CTAP and the prediction accuracies of CID