NDLI: Mass Estimation for an Adaptive Trajectory Predictor using Optimal Control

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Mass Estimation for an Adaptive Trajectory Predictor using Optimal Control

Content Provider	ACM Digital Library
Author	Prats, Xavier Vilardaga, Santi
Abstract	Air traffic predictability is paramount in the air traffic system in order to enable concepts such as Trajectory Based Operations (TBO) and higher automation levels for self-separation. Whereas in simulated environments 4D conflict-free trajectory optimisation has shown good potential in the improvement of air traffic efficiency, its application to real operations has been very challenging due to the current lack of information sharing between airspace users. Consequently, such operations are still very limited in scope and rarely attempted in dense traffic situations. Better predictability of other traffic future states would be an enabler for each aircraft to fly its user preferred route without decreasing safety in a self-separation context. But this is not an easy task when basic aircraft parameters such as aircraft weight, performance data or airline strategies are not available at the time of prediction. In this paper the authors propose to compensate this hindrance by continuously integrating the state of the surounding traffic to improve the ownship's knowledge of other aircraft's dynamics. Specifically, conventional position (and velocity) messages, as coming from Automatic Dependent Surveillance Broadcast (ADS-B), are integrated at the ownship. Then, an optimisation problem is formulated, using optimal control theory, that minimises the error with the known states, having the parameters of study (i.e. mass) as decision variables. A scenario with two departing trajectories is used to demonstrate the effectiveness of this parameter estimation method. In it, the take-off mass of the potential intruder is estimated on-board the ownship and its impact to conflict detection and resolution is presented, demonstrating the big improvements in predictability and safety.
Starting Page	75
Ending Page	84
Page Count	10
File Format	PDF
ISBN	9781450335621
DOI	10.1145/2899361.2899369
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trajectory prediction Mass estimation Optimal control Trajectory optimisation Self-separation
Content Type	Text
Resource Type	Article

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