P7. Advanced statistical signal processing for next generation trajectory prediction

2 – Data-driven trajectory prediction

Accurate and reliable trajectory prediction (TP) is required in several air traffic management (ATM) systems, for instance, to design air and ground-based decision support tools and safety nets. Estimating the aircraft trajectory in the vertical plane typically requires the knowledge of a pair of aircraft intents (e.g., constant Mach and minimum throttle), information which is seldom available, besides for the ownship (i.e., one’s own aircraft) trajectory planning system. In the flight execution phase, the aircraft is directed by the (auto) pilot through a series of sequential guidance modes that might override some of the planning phase aircraft intents. Thus, guidance mode is defined as a combination of constraints/commands that specify how the aircraft should behave to perform a desired trajectory.

Reliable guidance mode information is fundamental for next generation of air- or ground-based TP, especially in the context of trajectory-based operations (TBO) and advanced decision support tools for aircraft crew and/or air traffic control e.g., to improve conflict detection (and resolution) algorithms, conformance monitoring, departure/arrival managers, separation assurance systems, etc. These new tools might result in increased safety, capacity, predictability and cost-efficiency for the future European ATM system.

This research is concentrated on identifying aircraft guidance modes in the vertical plane. The final goal of this study is to indicate that acquiring the knowledge of aircraft guidance mode significantly affects the TP problem, and subsequently, the new ATM systems. In this PhD i) we provided a new probabilistic perspective of the trajectory prediction problem using signal processing mathematical tools, ii) we review state-of-the-art and the main challenges for the design of novel or enhanced TP systems that should enable future ATM paradigms, iii) we develop an optimal guidance mode identification using a Kalman filtering approach, iv) we analyse the impact of model mismatch on the interacting multiple model (IMM) filtering technique, v) we propose a robust linear-constrained IMM filtering under model mismatch, vi) we also propose a new methodology based on Bayesian inference to identify the aircraft guidance modes, and finally, vii) we evaluate the methodology to indicate the effect of known guidance modes on the TP accuracy.