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Distributed Joint Observation and Transmission Planning Method for Multiple Agile Satellites in Mega Constellation Networks
  • +6
  • Ningxuan Guo,
  • Yuqi Wang,
  • Yufei Li,
  • Liang Liu,
  • Yupeng Gong,
  • Ningyuan Wang,
  • Anshou Li,
  • Dan Liu,
  • Xiaoqing Zhong
Ningxuan Guo
Author Profile
Yuqi Wang
Yufei Li
Liang Liu
Yupeng Gong
Ningyuan Wang
Anshou Li
Dan Liu
Xiaoqing Zhong

Abstract

With the improvement of satellites’ maneuverability, agile earth observation satellites (AEOSs) can pitch and roll themselves to observe targets with longer visible time window (VTW), which enables more targets to be observed while bringing greater uncertainties of mission planning and more conflicts of resources. Meanwhile, mega constellation networks (MCNs) provide powerful tools to transmit massive observation data. In MCNs, AEOSs can observe targets agilely and access communication satellites (CSs) by inter-satellite links (ISLs) to offload data. Based on this architecture, we propose a Distributed Joint Observation and Transmission Planning method for Multiple AEOSs (MA-DJOTP). This method uses a targets allocation strategy, a CS allocation model, and a targets reallocation strategy to transform the multi-AEOS problem into several single-AEOS subproblems. The Single-AEOS Joint Observation and Transmission Planning (SA-JOTP) model is formulated as a Mixed Integer Quadratic Constraint Programming (MIQCP) problem based on a mission-based time slot division method, which can help simplify the observation time determination and ISL handover modeling. The SA-JOTP model can realize both the benefit maximization and the transmission delay minimization based on practical constraints of mission transition time, laser ISLs’ characteristics, and limited onboard resources. We verify the effectiveness of the proposed MA-DJOTP algorithm in MCNs with 720 CSs and 3, 16, 36, 360 AEOSs. The results show that the proposed algorithm can obtain a solution very close to the global optimum of the centralized method and is applicable in MCNs with hundreds of satellites.
29 Mar 2024Submitted to TechRxiv
01 Apr 2024Published in TechRxiv