Scheduling Optimization of Multiple Radars for Space Object Detection Mission

With the increasing frequency of human activities, more and more spacecraft are launched into orbit, making the space environment increasingly crowded. In order to ensure the safe operation of spacecraft in orbit, people usually use ground-based radar and telescopes to detect the space objects and determine their motion status. However, the rapidly increased amount of resident space objects makes the detection mission challenging for the existing ground-based sensor network, requiring the scheduling optimization of the detection plan. In this paper, the scheduling optimization procedure of space objection detection is first analyzed, and the spatiotemporal constraints among the ground-based sensors and space objects are precomputed by introducing the concept of detectable arc segment. Then the scheduling optimization model is developed under the framework of constraint satisfaction problem, where both the tracking requirements of space objects and the detection capabilities of ground-based radars are reformulated as constraints. An adaptive simulated annealing algorithm is proposed, which include a fitness-based initial solution generation strategy and some adaptive adjustment mechanisms like temperature adaptive update and neighborhood structure adaptive selection. Numerical simulations are conducted alongside the existing algorithms like differential evolution algorithm and ant colony optimization, validating the validity and performance of the scheduling optimization algorithm herein.