Optimal Satellite Constellation Configuration Design: A Collection of Mixed Integer Linear Programs
This work addresses the challenge for satellite system designers in performing trade-off studies across different mission scenarios, offering a unified framework, though it is incremental as it builds on existing optimization methods.
The paper tackles the complex optimization problem of designing satellite constellation configurations by proposing a collection of five mixed-integer linear programs that can handle various metrics and mission scenarios, such as percent coverage and revisit times, to obtain provably optimal configurations, as demonstrated through case studies.
Designing satellite constellation systems involves complex multidisciplinary optimization in which coverage serves as a primary driver of overall system cost and performance. Among the various design considerations, constellation configuration, which dictates how satellites are placed and distributed in space relative to each other, predominantly determines the resulting coverage. In constellation configuration design, coverage may be treated either as an optimization objective or as a constraint, depending on mission goals. State-of-the-art literature addresses each mission scenario on a case-by-case basis, employing distinct assumptions, modeling techniques, and solution methods. While such problem-specific approaches yield valuable insights, users often face implementation challenges when performing trade-off studies across different mission scenarios, as each scenario must be handled distinctly. In this paper, we propose a collection of five mixed-integer linear programs that are of practical significance, extensible to more complex mission narratives through additional constraints, and capable of obtaining provably optimal constellation configurations. The framework can handle various metrics and mission scenarios, such as percent coverage, average or maximum revisit times, a fixed number of satellites, spatiotemporally varying coverage requirements, and static or dynamic targets. The paper presents several case studies and comparative analyses to demonstrate the versatility of the proposed framework.