• Patent Title: LEARNING METHOD FOR A NEURAL NETWORK EMBEDDED IN AN AIRCRAFT FOR ASSISTING IN THE LANDING OF SAID AIRCRAFT AND SERVER FOR IMPLEMENTING SUCH A METHOD
  • Application No.: US16691031
    Application Date: 2019-11-21
  • Publication No.: US20200168111A1
    Publication Date: 2020-05-28
  • Inventor: Yoan VEYRACPatrick GARRECPascal CORNIC
  • Applicant: THALES
  • Priority: com.zzzhc.datahub.patent.etl.us.BibliographicData$PriorityClaim@3252ba10
  • Main IPC: G08G5/02
  • IPC: G08G5/02 G05D1/10 G06N3/08 G06T17/05
LEARNING METHOD FOR A NEURAL NETWORK EMBEDDED IN AN AIRCRAFT FOR ASSISTING IN THE LANDING OF SAID AIRCRAFT AND SERVER FOR IMPLEMENTING SUCH A METHOD
Abstract:
The method uses a fleet of aircraft being equipped with at least one radar sensor, it includes at least: a step of collective collection of radar images by a set of aircraft (A1, . . . AN) of the fleet, the radar images being obtained by the radar sensors of the aircraft (A1, . . . AN) in nominal landing phases on the runway, a step wherein each image collected by an aircraft is labelled with at least information on the position of the runway relative to the aircraft, the labelled image being sent to a shared database and stored in the database; a step of learning by a neural network of the runway from the labelled images stored in the shared database, at the end of the step the neural network being trained; a step of sending of the trained neural network to at least one of the aircraft (A1).
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