Particle swarm optimization based space debris surveillance network scheduling

• 来源期刊: 天文和天体物理学研究
• 年，卷（期）: 2017,（3）
• 页码范围: 30.1-30.10
• 页数: 1页
• 语种: 英文
• DOI: 10.1088/1674-4527/17/3/30

摘要

The increasing number of space debris has created an orbital debris environment that poses increasing impact risks to existing space systems and human space flights. For the safety of in-orbit spacecrafts, we should optimally schedule surveillance tasks for the existing facilities to allocate resources in a manner that most significantly improves the ability to predict and detect events involving affected spacecrafts. This paper analyzes two criteria that mainly affect the performance of a scheduling scheme and introduces an artificial intelligence algorithminto the scheduling of tasks of the space debris surveillance network. A new scheduling algorithmbased on the particle swarmoptimization algorithmis proposed, which can be implemented in two different ways: individual optimization and joint optimization. Numerical experiments with multiple facilities and objects are conducted based on the proposed algorithm, and simulation results have demonstrated the effectiveness of the proposed algorithm.