Sequential Monte Carlo methods for multi‐aircraft trajectory prediction in air traffic management

Abstract

AbstractAccurate prediction of aircraft trajectories is an important part of decision support and automated tools in air traffic management. We demonstrate that by combining information from multiple aircraft at different locations and time instants, one can provide improved trajectory prediction (TP) accuracy. To perform multi‐aircraft TP, we have at our disposal abundant data. We show how this multi‐aircraft sensor fusion problem can be formulated as a high‐dimensional state estimation problem. The high dimensionality of the problem and nonlinearities in aircraft dynamics and control prohibit the use of common filtering methods. We demonstrate the inefficiency of several sequential Monte Carlo algorithms on feasibility studies involving multiple aircraft. We then develop a novel particle filtering algorithm to exploit the structure of the problem and solve it in realistic scale situations. In all studies we assume that aircraft fly level (possibly at different altitudes) with known, constant, aircraft‐dependent airspeeds and estimate the wind forecast errors based only on ground radar measurements. Current work concentrates on extending the algorithms to non‐level flights, the joint estimation of wind forecast errors and the airspeed and mass of the different aircraft and the simultaneous fusion of airborne and ground radar measurements. Copyright © 2010 John Wiley & Sons, Ltd.