Multi-Satellite Scheduling for Moving Targets Using Enhanced Hybrid Genetic Simulated Annealing Algorithm and Observation Strip Selection

This paper investigates a multi-satellite scheduling method for observing moving targets based on the hybrid heuristic algorithm and observation strip selection method. Firstly, the constraint satisfy problem model for multi-satellite guided collaboration is established considering multiple constraints. Subsequently, based on the genetic algorithm framework incorporated by congestion-based simulated annealing, the enhanced hybrid genetic simulated annealing algorithm is proposed to assign electronic and imaging satellite missions. The uneven distribution of workload among satellites can be balanced by implementing a multi-layer encoding method and neighborhood generation based on congestion, which effectively reduces conflicts and promotes fast convergence. Additionally, for imaging observation strip selection, the short-term track prediction model for moving targets is developed to determine the potential target area, and the area dynamic decomposition is adopted to further locate the specified imaging strip with the highest discovery probability. Finally, numerical results of both hetero-orbit and co-orbit constellations verify the feasibility and effectiveness of the proposed multi-satellite scheduling method.