Abstract
Abstract High-altitude satellites are visible to more ground station antennas for longer periods of time, its requests often specify an antenna set and optional service windows, consequently leaving huge scheduling search space. The exploitation of reinforcement learning techniques provides a novel approach to the problem of high-altitude orbit satellite range scheduling. Upper sliding bound of request pass was calculated, combining customized scheduling strategy with overall antenna effectiveness, a frame of satellite range scheduling for urgent request using reinforcement learning was proposed. Simulations based on practical circumstances demonstrate the validity of the proposed method.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
