Particle swarm optimization based space debris surveillance network scheduling

2017-05-14 23:08:33

Hai Jiang1,2,3 , Jing Liu1,2,3, Hao-Wen Cheng1,3 and Yao Zhang1,2,3

1 National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China;

2 University of Chinese Academy of Sciences, Beijing 100049, China

3 Space Debris Observation and Data Application Center, China National Space Administration, Beijing 100012, China

Received 2016 September 2; accepted 2017 January 12

Abstract 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 algorithm into the scheduling of tasks of the space debris surveillance network. A new scheduling algorithm based on the particle swarm optimization algorithm is 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.

Key words: methods: data analysis — observational catalogs — telescopes — techniques: radar astronomy

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