Optimal Observation Planning Method for Agile Earth Observation Satellites Based on Improved Ant Colony Algorithm

This study focuses on generating the optimal sequence of point target observations in dense areas, adopting a new optical agile satellite imaging approach: staring observation, and proposes an efficient and high-yield observation target clustering method along with a heuristic ant colony optimization algorithm. Firstly, aiming at the staring observation characteristics of agile satellites, this paper designs a group division strategy based on the earliest end time and, by synthesizing dense point targets to reduce the satellite’s maneuvering frequency and energy consumption, thereby enhancing observation efficiency. Secondly, considering constraints such as the satellite’s visible time windows, maneuverability, resource limitations, and the solar elevation angle, we constructed a multi-objective optimization model targeting observation benefits and energy consumption. To solve this model, an improved heuristic ant colony optimization (IHACO) algorithm is proposed, which takes task intervals, priorities, and feasible start time lengths as heuristic information and introduces Lévy flight to improve the pheromone evaporation mechanism, thus enhancing the algorithm’s ability to avoid falling into local optima. Finally, the efficiency of the algorithm is verified through simulation experiments and application scenarios of large-scale dense point targets.