Particle Swarm Optimization Based Space Debris Surveillance Network Scheduling

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