# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 5 Discussion  
From the event flowcharts of the simulation results, it is shown that by incorporating the data transmission path planning method into the observation scheduling method, the entire process of coupling the scheduling of observation, relay transmission, and downloading can be achieved. This process enables the observation satellite to download the data through other satellites relaying in the non-data transmission window orbit period to reduce the storage occupation, so that the satellite has more available storage capacity to complete other target observations and improves the observation ability of the satellite cluster. The statistical simulation results reveal that, in the scenario of insufficient electric energy, the average optimal solution objective function values of CTSRD and CTSND are nearly the same. The average optimal solution objective function value of CTSRD is 23.9% higher than that of CTSTD. The average optimal solution iteration time of CTSND, CTSTD, and CTSRD is nearly the same. In the simulation scenario of insufficient data transmission resources, the average objective function values of the optimal solutions of CTSRD and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Optimal Solution**} & \multicolumn{3}{c}{**Optimal Solution**} \\  & \multicolumn{3}{c}{**Objective Function Value**} & \multicolumn{3}{c}{**Iteration Time(s)**} \\ \cline{2-7}
**Method** & **Max** & **Min** & **Average** & **Max** & **Min** & **Average** \\ \hline CTSND & 60.19 & 55.31 & 58.03 & 47,471.11 & 32,533.38 & 42,458.55 \\ CTSTD & 64.87 & 59.21 & 61.52 & 52,480.11 & 11,987.33 & 39,925.27 \\ CTSRD 1  & 64.87 & 59.21 & 61.52 & 52,510.30 & 11,987.42 & 39,951.82 \\ SASRD & 64.75 & 59.28 & 61.99 & 56,374.09 & 40,695.00 & 49,539.53 \\ GASRD & 65.64 & 59.46 & 62.37 & 68,194.30 & 49,960.36 & 61,536.70 \\ ITSRD 2  & 67.67 & 63.20 & 64.86 & 51,163.97 & 11,726.50 & 30,114.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical results of different methods in scenario 2. Figure 12: Observation and data transmission event diagram of ITSRD in scenario 2. CTSTD are the same, and the time to reach the optimal solution is nearly the same. The average optimal solution objective function value of CTSRD is 6% higher than that of CTSND, and the iteration time to reach the optimal solution of CTSRD is 5.9% less than that of CTSND. The comparison among different methods of data transmission path planning shows that the proposed rule-based Dijkstra data transmission planning method has better adaptability in different conditions of satellite cluster states and environments. At the same time, it also shows that the two Dijkstra planning methods based on strategies are effective only in the corresponding conditions. In the scenario of insufficient electric energy, the average optimal solution objective function value of ITSRD is 3.8% higher than that of CTSRD, and the average time required to reach the optimal solution of ITSRD is 31.8% less than that of CTSRD. In the scenario of insufficient data transmission resources, the average optimal solution objective function value of ITSRD is 5.4% higher than that of CTSRD, and the average time required for ITSRD to reach the optimal solution is 24.6% less than that of CTSRD. The result comparisons between ITSRD and CTSRD show that the improved local search process in tabu search improves the searching efficiency. In the two scenarios, ITSRD achieves the best optimal solution and the shortest iteration time for reaching the optimal solution of all methods, which also reflects the improvement effect of the method proposed in this article. In the multi-satellite observation-relay transmission-download coupling scheduling problem, the improved tabu search method in this article outperforms the conventional simulated annealing algorithm and the genetic algorithm in terms of calculation results and calculation time.