CLASSIFYING ADS-B TRAJECTORY SHAPES USING A DENSE FEED-FORWARD NEURAL NETWORK

There is a recent abundance of flight trajectory data due to Automatic Dependent Surveillance-Broadcast (ADS-B) becoming a prevalent and required aviation traffic control system. Motivated by incidents like the September 11, 2001, attacks, the Department of Defense and civilian intelligence agencies have taken a renewed interest in being able to quickly flag and act on flight pattern behavior that is considered outside the norm. Due to the large volume of daily flights in the United States alone, it is almost impossible for human operators to monitor and analyze individual flights for anomalous behavior. The Department of Defense and civilian intelligence agencies stand to gain increased capability and capacity if given the ability to analyze and flag unusual flight trajectories in a matter of seconds. Anomalous behavior in many cases is determined by the overall shape of the flight pattern. This thesis uses calculated shape features to classify nine pre-determined categories of ADS-B flight trajectories using a Deep Sequential Neural Network. With a data set of 11,303 human-classified tracks, the network has performed with an overall accuracy of 71% and a categorical average F1 score of 0.33 on a validation set. It has also performed with 70% accuracy and a categorical average F1 score of 0.25 on a ten-fold cross validation. The proposed method shows promise in being able to select unusual shapes from straight trajectories and in some cases may be able to classify them.Ensign, United States NavyApproved for public release. distribution is unlimite

Shape Analysis of Flight Trajectories Using Neural Networks

The article of record as published may be found at https://doi.org/10.2514/1.I010923The recent widespread implementation of Automatic Dependent Surveillance–Broadcasting (ADS-B) systems on aircraft allows for improved monitoring and air traffic control management. As part of this monitoring, it is important to be able to detect unusual flight trajectories due to weather events, detection avoidance, aircraft malfunction, or other activities that may signal anomalous behavior. Given the large volume of ADS-B data available from aircraft around the world, the ability to automatically determine the shape of the trajectory and identify anomalous behavior is important to reduce the need for human identification and labeling. A neural network model is developed for multicategory classification of the shape of the trajectory using features derived from a large ADS-B data set such as bearing and curvature. The results suggest promise in differentiating common trajectory shapes using key factors, with the accuracy of the classifier being comparable to human accuracy.Center for Multi-Intelligence Studies, Naval Postgraduate Schoo