Revision as of 18:16, 28 January 2021

Abstract

traffic predictability is paramount in the air traffc 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  traffc  effciency,  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 traffc 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 traffc 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 e ectiveness 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.

Peer Reviewed

Document type: Conference object

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The different versions of the original document can be found in:

• http://hdl.handle.net/2117/86426 under the license https://creativecommons.org/licenses/by-nc-nd

• https://upcommons.upc.edu/bitstream/2117/86426/1/vilardaga_15_mass.pdf under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

• https://upcommons.upc.edu/handle/2117/86426,

https://core.ac.uk/display/41825442,

https://doi.org/10.1145/2899361.2899369,

https://dblp.uni-trier.de/db/conf/atacss/ataccs2015.html#VilardagaP15,

https://dl.acm.org/citation.cfm?doid=2899361.2899369,

https://upcommons.upc.edu/bitstream/2117/86426/1/vilardaga_15_mass.pdf,

https://academic.microsoft.com/#/detail/2341039445 under the license cc-by-nc-nd

• http://dl.acm.org/ft_gateway.cfm?id=2899369&ftid=1708651&dwn=1,

http://dx.doi.org/10.1145/2899361.2899369

under the license http://www.acm.org/publications/policies/copyright_policy#Background