QUT predicts erratic flight paths with multiple machine learning techniques

Researchers at the Queensland University of Technology have developed an algorithm that uses a pair of machine learning techniques to quickly and accurately predict aircraft trajectories and flight paths. The algorithm was designed to increase the understanding of how airspace is used from a defence perspective, but could also be applied to civilian air traffic control or any scenario in which movement needs to be analysed. Data is fed into two neural networks - deep neural networks, which analyse data at multiple levels to predict the probability of outcomes with increasing accuracy; and memory networks, which feature a memory component that can be read from and written to. "In essence, it's built to measure a trajectory in and predict a trajectory out," professor Clinton Fookes from the university's Vision and Signal Processing discipline said. "But as it's taking in the trajectory of the target object, it's also taking in the trajectories of neighbouring objects to create an awareness of what's around the target and how those objects are moving."