Agile satellites possess roll and pitch maneuver capabilities, and adopting the strip-splicing mode for area target observation can improve the coverage rate. However, this observation process requires simultaneous optimization of roll angles, pitch angles, and the observation start and end times of each strip. To address this multistrip observation scheduling problem, an agile satellite area target observation scheduling method based on grid subdivision is proposed. The method employs hexagonal grids to perform a double-layer discretization of the target area, which is utilized for attitude mapping, coverage calculation, and heuristic guidance. With the goal of maximizing the coverage rate, a mixed-integer nonlinear programming model considering multiple constraints is established. To solve the model, a bilevel optimization framework based on adaptive feedback iteration is designed, and a bilevel optimization algorithm integrating simulated annealing with cooperative particle swarm genetic algorithm is constructed. Specifically, the upper layer performs observation strip allocation via simulated annealing and its multi-neighborhood search strategy, while the lower layer employs the cooperative particle swarm genetic algorithm to schedule pitch angles and observation windows. And the interactive iterative solution of the upper and lower layer algorithms is realized through the adaptive feedback mechanism. Extensive comparative experiments demonstrate that in agile satellite area target observation scenarios, the proposed method is superior to various comparison algorithms in terms of coverage rate and convergence stability. Moreover, compared with the traditional single-strip Earth observation mode, the strip-splicing mode of agile satellites can significantly improve the area coverage rate.

As the observation data generated by Earth Observation Satellites (EOSs) increase, the joint scheduling of satellite imaging and data transmission has become a bottleneck in EOS resource applications. This study has proposed a three-stage-based hybrid meta-heuristic scheduling algorithm for the Agile Satellite Joint Imaging and Data Transmission Scheduling (ASJIDTS) problem. The original complex problem is decomposed into three distinct phases: joint task allocation for imaging and data transmission, scheduling of imaging tasks, and scheduling data transmission tasks. During the initial phase of joint task allocation, both imaging and data transmission resources are preemptively allocated using a greedy-based strategy, which considers data transmission opportunities, the conflict degree, and the spatial distribution of different resources. Subsequently, the imaging task scheduling phase generates an optimized sequence for imaging tasks. Based on this sequence, a rule-based multi-insertion strategy for the data transmission scheduling phase has been designed, which ensures rapid responsiveness to data transmission tasks. Extensive experiments have been conducted to verify the proposed algorithm. For the scheduling scenarios with 200 tasks, The Hybrid Metaheuristic Algorithm based on Multi-Stage (HMA-MS) shows at least a 14.11% increase in scheduling profit compared to several excellent algorithms. The experimental results validate the superior capability of the proposed algorithm in handling large-scale scheduling problems.

The practical engineering of satellite tracking telemetry and command (TT&C) is often disturbed by unpredictable external factors, including the temporary rise in a significant quantity of satellite TT&C tasks, temporary failures and failures of some TT&C resources, and so on. To improve the adaptability and robustness of satellite TT&C systems when faced with uncertain dynamic disturbances, a hierarchical disturbance propagation mechanism and an improved contract network dynamic scheduling method for satellite TT&C resources were designed to address the dynamic scheduling problem of satellite TT&C resources. Firstly, the characteristics of the dynamic scheduling problem of satellite TT&C resources are analyzed, and a mathematical model is established with the weighted optimization objectives of maximizing the revenue from task completion and minimizing the degree of plan disturbance. Then, a bottom-up distributed dynamic collaborative scheduling framework for satellite TT&C resources is proposed, which includes a task layer, a resource layer, a central internal collaboration layer, and a central external collaboration layer. Dynamic disturbances are propagated layer by layer from the task layer to the central external collaboration layer in a bottom-up manner, using efficient heuristic strategies in the task layer and the resource layer, respectively. We use improved contract network algorithms in the center internal collaboration layer and the center external collaboration layer, the original scheduling plan is quickly adjusted to minimize the impact of disturbances while effectively completing dynamic task requirements. Finally, a large number of simulation experiments were carried out and compared with various comparative algorithms. The results show that the proposed algorithm can effectively improve the solution effect of satellite TT&C resource dynamic scheduling problems, and has good application prospects.