In the realm of missile defense systems, the self-sufficient maneuver capacity of missile swarms is pivotal for their survival. Through the analysis of the missile dynamics model, a time-efficient cooperative attack strategy for missile swarm is proposed. Based on the distribution of the attackers and defenders, the collision avoidance against the defenders is considered during the attack process. By analyzing the geometric relationship between the relative velocity vector and relative position vector of the attackers and defenders, the collision avoidance constrains of attacking swarm are redefined. The key point is on adjusting the relative velocity vectors to fall outside the collision cone. This work facilitates high-precision attack toward the target while keeping safe missing distance between other attackers during collision avoidance process. By leveraging an innovative repulsion artificial function, a time-efficient cooperative attack strategy for missile swarm is obtained. Through rigorous simulation, the effectiveness of this cooperative attack strategy is substantiated. Furthermore, by employing Monte Carlo simulation, the success rate of the cooperative attack strategy is assessesed and the optimal configuration for the missile swarm is deduced.

Space Station Operation Mission Planning (SSOMP) requires coordinated decisions across long-term activity allocation, mid-term logistics optimization, and short-term execution scheduling and is a key component of autonomous mission operations for high-precision space missions. Existing optimization methods have achieved substantial progress at individual planning levels, but their dependence on problem-specific models, limited support for semantic review of decision rationale, and computational cost restrict their adaptability to multi-level planning scenarios. This paper proposes a Large Language Model (LLM)-assisted framework for multi-level SSOMP. The framework combines Staged Fine-Tuning (Staged-FT), Reflective Constraint–Repair Prompting (RCRP), and LLM-Guided Evolutionary Variation (LGEV). Staged-FT uses a Cognitive-Load-Theory-informed curriculum with Low-Rank Adaptation to adapt general-purpose LLMs to SSOMP domain knowledge. RCRP couples a Deterministic Rule Engine with LLM-based semantic repair to improve hard constraint satisfaction. LGEV embeds the fine-tuned LLM into NSGA-III as a fitness-aware variation operator for multi-objective activity allocation. Three case studies are conducted on literature-derived benchmark scenarios of logistics optimization, emergency re-planning, and activity allocation with logistics design, corresponding to Flight Increment Planning, Short-Term Execution Planning, and Overall Operation Planning, respectively. Results show that Staged-FT produces solutions close to traditional algorithms, RCRP achieves full hard constraint satisfaction in the emergency re-planning and logistics planning cases, and LGEV reduces the convergence generations of NSGA-III while improving Pareto-front quality. The framework provides a constraint-aware approach with explicit reasoning traces that can support expert review of AI-assisted planning for autonomous space mission operations.

It is challenging to detect continuous low-thrust maneuver parameters of non-cooperative satellites because the signals are weak over limited observation arcs and are readily masked by measurement noise and orbit-determination errors. This paper proposes a conditional diffusion model for detecting and estimating continuous low-thrust maneuver parameters from relative-orbit observations. The method uses relative-orbit observations of the non-cooperative target to construct conditional inputs that incorporate orbital dynamical priors. Single-step differencing and dimensionless processing are then used to strengthen weak maneuver signatures. The conditional diffusion model learns the evolution of maneuver parameters under noisy conditions and estimates three-axis continuous low-thrust acceleration sequences. Based on simulations considering the Gaussian noise of relative positions and velocities, the proposed method achieved 85.2% maneuver detection accuracy, while that of the batch least-squares benchmark method was 67.8%. The proposed method is simulated and verified based on Sentinel-6A. Results show that the continuous low-thrust maneuver can be robustly identified under low signal-to-noise ratios and the temporal parameter evolution can be recovered. The method provides a practical route for analyzing non-cooperative satellite maneuver and supporting on-orbit space situational awareness.