Optimal Satellite Constellation Configuration Design: A Collection of Mixed Integer Linear Programs

TL;DR

This paper introduces a versatile collection of mixed-integer linear programs for designing optimal satellite constellations, capable of handling diverse coverage metrics and mission scenarios with proven optimality.

Contribution

The paper presents a set of five adaptable MILP models for satellite constellation design, enabling systematic trade-offs and optimal solutions across various mission requirements.

Findings

Models successfully optimize coverage and revisit times.

Framework demonstrates flexibility across different scenarios.

Case studies validate effectiveness and versatility.

Abstract

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.