Traditional Low Earth Orbit (LEO) satellite constellation design methods, primarily driven by geometric coverage, fail to satisfy the non-uniform and dynamic tracking requirements of moving targets. To address this, a multi-objective optimization framework for large-scale satellite constellations is proposed. This framework is task-driven, constraint-guided, and integrates space and ground segments. A quantitative model is established to characterize the multi-target tracking capability of space-based sensing systems. The model explicitly links the constellation revisit period, payload detection and positioning performance, target maneuverability, and the maximum trackable target density. These relationships are then formulated as optimization objectives and constraints. To capture temporal consistency in observation performance, the coefficient of variation of revisit time is introduced as an independent optimization objective. This yields a three-objective optimization problem that addresses tracking performance, coverage uniformity, and system cost, enabling Pareto-optimal constellation design solutions. Simulation results demonstrate that the proposed method improves track association performance in representative maritime target tracking scenarios when compared with conventional coverage-driven constellation designs. The proposed framework provides a systematic and implementable approach for constellation design by integrating capability modeling with multi-objective optimization at the system level.
Wang et al. (2026) studied this question.