Machine Learning in Phase of Flight Detection

The study integrates a thorough investigation of machine learning approaches with unsupervised clustering algorithms to advance the area of flight phase identification in aviation. We initiate our research with a comprehensive assessment of the literature on machine learning applications for flight phase detection, both scientometrically and conceptually. This review looks at how machine learning techniques have developed to meet theoretical and practical goals in this field, as well as publishing trends, relevant papers and top research institutes.
A significant contribution of our work is the application of several unsupervised clustering algorithms to flight data, which is simulated using Monte Carlo Simulation. This allows us to identify crucial flying phases, namely the straight and level phase and the turn phase. Through the performance evaluation of these techniques, we find that among the models examined, the Gaussian Mixture Model (GMM) provides the most accurate phase detection. This result enhances the accuracy of flight phase identification and demonstrates the advantage of GMM in handling the intrinsic complexity of flight data.
Furthermore, our analysis points out areas of current research deficiency and proposes future directions for investigation, offering a roadmap for the development of machine learning methods in aviation that could improve operational effectiveness, safety and training programs.