Abstract: To address the problem that the continuous expansion of satellite cluster scales and dynamic variations in space target types induce an exponential growth in the dimensionality of the optimal solution space for observation tasks, this study investigated multiangle and omnidirectional sustained observation technologies for space debris, with the aim of improving the observation coverage effectiveness, algorithm convergence stability and multi-scenario adaptability of satellite clusters within the observation time domain.A mathematical characterization model for the space debris observation region was constructed to realize the accurate quantification of the target observed area and blind zone.An improved genetic algorithm was subsequently designed by integrating an adaptive mutation strategy, a roulette-wheel selection operator and an observation task fitness function, thereby achieving the collaborative optimization of initial position deployment for satellite clusters and real-time field-of-view pointing to capture effective surface feature information of space debris throughout the observation time domain.Simulation verification was carried out in two task scenarios configured in the geostationary Earth orbit, namely dispersed debris and concentrated debris.The simulation results show that compared with the dung beetle optimization algorithm, the proposed layout optimization algorithm for space debris group monitoring tasks based on the improved genetic algorithm improves the convergence performance by 10%, achieves a sustained effective observation coverage ratio of more than 82% under scenarios with different satellite scales and target densities, and exhibits high coverage efficiency, strong convergence stability and full-scenario adaptability.This algorithm meets the efficient and sustained observation requirements of sparse to dense space debris groups with various satellite cluster scales, and provides technical support for the task optimization of sustained space debris observation using satellite clusters.

Key words: multi-satellite TT&, C；task planning；genetic algorithms；high-orbit satellite；TT&, C scheduling