# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 1 Introduction
The multi-subsystem constraints, including attitude maneuvering constraints, electricity subsystem constraints, data transmission subsystem constraints, etc., are considered in the scheduling problem. To address the multi-satellite observation-relay transmission-download coupling scheduling problem, a multi-satellite data transmission path planning method is incorporated into the conventional multi-satellite observation scheduling method in this article. In the multi-satellite observation-relay transmission-download coupling scheduling problem, the target is relatively fixed while the ground station and satellites' orbits are fixed. Although the motion parameters of satellites, the relative position of a satellite to a target, and the relative position of a satellite to a ground station change during the observation process, these changes are regular and can be converted into fixed relationships through pre-processing calculations. These relationships include the observation time window of a satellite to a target, the data transmission time window of a satellite to a ground station, the inter-satellite link topology, etc. The data transmission path planning problem is different from the network routing path planning problem [26]. In the network routing path planning problem, dynamic factors, including varying network load, transmission delay, and network bandwidth, are required to be considered in the routing algorithm because of the varying service flow and the number of access users. However, for the data transmission path planning process in the multi-satellite observation-relay transmission-download coupling scheduling problem, the inter-satellite and satellite-ground data transmission windows are determined by the initial distribution of satellites and ground stations, and network topology changes can be predicted. At the same time, inter-satellite and satellite-to-ground data transmissions are point-to-point transmissions, resulting in relatively minor variations in bandwidth and network load. Therefore, in this article, the variation of dynamic parameters in the communication process is not the main contradiction. In this context, calculating inter-satellite and satellite-grounddata transmission time windows during preprocessing, scheduling the order of data transmission nodes, and arranging data transfer events is a common method in observation data transmission planning [27]. The data transmission path planning problem is considered the data transmission node path selection certainty problem in this article, which can meet the demand for the solution of the data transmission planning process in the multi-satellite observation-relay transmission-download coupling scheduling problem. In this case, combined with the reliability requirements in the astronautic field, the classic Dijkstra method [28] is applied to solve the problem of data transmission path planning. When solving the data transmission path planning problem, in certain scenarios, it is necessary to download the observation data earlier to reduce the occupation of data transmission resources, whereas in other scenarios, the number of data relay satellite nodes needs to be minimized to reduce satellite electric energy consumption, in which case the path search strategy has to be changed according to the satellite cluster states. To cope with different scenarios, the appropriate data transmission path selection under different satellite cluster states is achieved by formulating different node distance calculation rules and selecting the path according to the distance calculation rules in specific satellite cluster states. The multi-satellite observation-relay transmission-download coupling scheduling process is complex, large-scale, and also involves numerous subsystem constraints. There is coupling and mutual influence among the subsystems of the satellite. In multi-satellite observation scheduling, commonly used methodologies include exact methods, heuristic methods, and metaheuristic methods [29].