Improving Genetic Task Planning Method for Observing Moving Targets with Dual-satellite

Dual-satellite observation of moving targets involves using two sensor payloads to simultaneously observe and track a moving target. This planning problem is complex, due to challenges such as high target dynamics, visibility analysis, temporal constraints, and resource limitations in selecting two satellites from a larger constellation. To address this, an improved genetic algorithm (IDGPA) for dual-satellite observation task planning is proposed. The method constructs a discrete task planning model based on moving target trajectory predictions, transforming continuous planning problems into discrete planning problems. IDGPA utilizes a dual-population genetic algorithm with differentiated initialization and selection strategies to balance global and local search capabilities, alongside adaptive crossover and mutation operators. Simulation results demonstrate that IDGPA significantly improves solution quality and computational efficiency under complex constraints.