Real-time Aircraft Tracking System: A Survey and A Deep Learning Based Model

Real-time tracking of a maneuvering aircraft is a challenging issue in the literature. For effective tracking, an accurate and complete transfer of data from aviation to ground systems is needed. However, possible data loss caused by telemetry or data acquisition system makes aircraft tracking difficult. To mitigate this problem, efficient tracking systems are proposed in the literature. Accordingly, we first present a brief survey of aircraft tracking systems by splitting them according to their approaches as mathematical, machine learning-based, and deep learning-based approaches. After examining the existing studies, we offer a real-time Deep learning-based Aircraft Tracking (DeepAT) system that enables real-time tracking of an aircraft. Deep learning models are employed to predict the next location of aircraft. Accordingly, the radar angle of the antenna is determined to the antenna or radar system directs to the next location. When the proposed model is analyzed through two potential use case scenarios which are flight tests and military applications, DeepAT offers promising solutions to prevent data loss in different applications.