[Poster] Hidden Markov Models and Flight Phase Identification

Abstract

The use of Hidden Markov Models (HMMs) in segmenting flight phases is a compelling approach with significant implications for aviation and aerospace research. It leverages the temporal sequences of flight data to delineate various phases of an aircraft's journey, making it a valuable tool for enhancing the analysis of flight performance and safety. In this work, we implement a multivariate HMM to identify 6 flight phases: taxi, takeoff, climb, cruise, approach and rollout. We reach a median global accuracy of about 97\% over a sample of several thousand flights with a very low number of decoded unlikely transitions. Regarding several performance metrics, our method is competitive with existing methods in the literature, such as fuzzy logic. Additionally, it provides, for each point of the flight, a probability of belonging to each phase. Even in situations where there are missing values in the data, HMMs remain effective, ensuring that no critical information is lost during the segmentation process.