Air-Ground Trajectory Synchronization through Exchange of Aircraft Intent Information

Abstract

This paper presents preliminary results on how the exchange of Aircraft Intent (AI) information derived from a Flight Management System (FMS) can improve trajectory predictions made by different operational ground-based Decisions Support Tools (DSTs). AI, which contains information about the guidance strategy planned by the FMS, was expressed using the Aircraft Intent Description Language (AIDL). This formal language allows the encoding of any possible guidance mode or flight commands available to a pilot or FMS. Translators for airborne and ground-based operational DSTs were developed to facilitate the exchange and use of AI information in the AIDL format. The different operational DSTs considered in this study included an Indra Flight Data Processing System, a Barco Arrival Manager, and a General Electric Aviation Systems (GEAS) FMS. An experiment was conducted in which AI information was exchanged between a GEAS FMS and the different DSTs. The experiment was purposely conducted in nominal conditions (e.g., no wind, standard atmosphere, no air traffic control [ATC] intervention, FMS in managed mode, etc.) to isolate the effect of the exchange and use of AI on the ground predictions. The results indicate that the use of FMS-derived AI information improves the trajectory prediction process of ground-based DSTs. These results support the assertion that higher levels of trajectory synchronization between air and ground can be achieved when AI information (aircraft guidance strategy, already available in the FMS but not to ground DSTs) is exchanged, improving traditional flight intent exchange as found in the current form of a flight plan. A higher level of synchronization would, in turn, foster a coordinated decision-making process between airborne and ground DSTs.