Lee, K.; Kim, D.; Chung, D.; Lee, S. Application of Optimal Scheduling for Synthetic Aperture Radar Satellite Constellation: Multi-Imaging Mission in High-Density Regional Area. Aerospace2024, 11, 280.
Lee, K.; Kim, D.; Chung, D.; Lee, S. Application of Optimal Scheduling for Synthetic Aperture Radar Satellite Constellation: Multi-Imaging Mission in High-Density Regional Area. Aerospace 2024, 11, 280.
Lee, K.; Kim, D.; Chung, D.; Lee, S. Application of Optimal Scheduling for Synthetic Aperture Radar Satellite Constellation: Multi-Imaging Mission in High-Density Regional Area. Aerospace2024, 11, 280.
Lee, K.; Kim, D.; Chung, D.; Lee, S. Application of Optimal Scheduling for Synthetic Aperture Radar Satellite Constellation: Multi-Imaging Mission in High-Density Regional Area. Aerospace 2024, 11, 280.
Abstract
This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
