Aircraft Location Prediction using Deep Learning

Localization of aircraft is important to control air traffic safely and effectively. Although the Automatic Dependent Surveillance Broadcast (ADS-B) has many advantages, the transfer of control over the reported location to the aircraft brings a number of safety and security issues. In order to mitigate these issues and determine the locations of the aircraft which do not have position reporting capabilities or may report wrong locations, complementary or redundant localization methods that are independent of the aircraft are needed. The goal of this paper is to study the feasibility to localize aircraft (estimate the longitude, latitude, and altitude of an aircraft) based on crowdsourced air traffic control communication data, specifically time of arrival and signal strength measurements reported by many different sensors. Specifically, we design and test a deep neural network model for aircraft location prediction using realworld data from OpenSky Network, a crowd-sourced receiver network that obtains volumes of air traffic data from thousands of sensors. It is demonstrated that the proposed deep neural network outperforms the time difference of arrival (TDOA) and support vector regressor (SVR) in terms of the mean absolute percentage error (MAPE), and the proposed deep learning based method using crowdsourced air traffic control communication data is an effective solution for accurate aircraft location prediction that are independent of the aircraft.