Ground-based Estimation of Aircraft Mass, Adaptive vs. Least Squares Method

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of automated tools that detect and solve conflicts between trajectories. This paper focuses on the climb phase, because predictions are much less accurate in this phase than in the cruising phase. Trajectory prediction usually relies on a point-mass model of the forces acting on the aircraft to predict the successive points of the future trajectory. The longitudinal acceleration and climb rate are determined by an equation relating the modeled power of the forces to the kinetic and potential energy rate. Using such a model requires knowledge of the aircraft state (mass, current thrust setting, position, velocity, etc.), atmospheric conditions (wind, temperature) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems. In this paper, we improve the trajectory prediction accuracy by learning an unknown point-mass model parameter from past observations. This unknown parameter, the mass, is adjusted by fitting the modeled specific power to the observed energy rate. Two methods addressing this issue has been recently published. In this paper, we describe an improvement of the mass estimation method presented in one of them. Using a set of trajectories, the robustness of the two methods is compared by adding a noise on the observed variables.