## 4 Simulation and Analysis
### Comparison between SS-LCBBA and Baseline Algorithms
#### 4.2.4 Results  
In this paper, simulating and analyzing the coherent tracking process of the satellites is focused on maneuver trajectories. The single-overlay mission planning results of the baseline algorithm and SS-LCBBA algorithm were recorded in the form of Gantt charts. Then, the cumulative tracking score of the value function was used to reflect the improvement of the optimal switching windows on the observation score of the coherent tracking mission, for which the value function was used in Equation (34).  
Case 1: Trajectory 1  
The task planning results of the SS-LCBBA and the baseline algorithm are shown in Figure 12(a). The optimal switching window decision algorithm in the SS-LCBBA provides a more suitable switching period time for the satellite. As a result, the SS-LCBBA obtained a higher score. As shown in Figure 12(b), the baseline algorithm chose different satellite sequences, but the scores are relatively close. Hence, the SS-LCBBA could choose a more suitable switching time and effectively suppressed the score decay during satellite switching.  
Case 2: Trajectory 2  
As shown in Figure 13(a), the vehicle's longitudinal maneuver was tiny, the flight-path angle of the \(QEG\) trajectory changed less, and the altitude of the vehicle became a steadily decreasing trend. Even though the vehicle performed evasive maneuvers laterally, the constellation tracking satellites monitored at a higher frequency to achieve full tracking  
Figure 13: The planning results of tracking trajectory 1: (**a**) is a Gantt chart comparing the planning results and (**b**) is the cumulative tracking score of the value function.  
of the vehicle. The ICNP algorithm has a message center. The planning algorithm prefers satellites with longer tracking times to reduce the computational and communication delays caused by switching satellites. The remaining three mission planning methods are all distributed planning methods. The LCBBA is locally consistent, and its faster response time allows for more satellite switching. Therefore, as shown in Figure 14b, LCBBA and SS-LCBBA selected the satellite sequence with a closer observation distance and obtained higher scores. The SS-LCBBA achieved a higher score when tracking lateral coherent maneuver vehicles.  
Case 3: Trajectory 3  
As shown in Figure 15a, the optimal switching window overlaps with the tracking window of the tracking satellite. The reassigned satellite could start tracking the vehicle when the tracking satellite had not reached the tracking limit, given that the observation conditions were better, and the cumulative tracking score was higher. The initial altitude of the vehicle was relatively high, and the constellation had excellent visibility. Therefore, the mission planning algorithm can detect planning results with higher scores faster. As shown in Figure 15b, the four algorithms can obtain relatively close scores in the early stage. Due to the significant lateral maneuver and lower flight altitude in the mid-flight of the vehicle, the mission planning algorithm needs to switch tracking satellites frequently to satisfy the full segment tracking of the vehicle by the constellation.  
Figure 14: The planning results of tracking trajectory 2: (**a**) is a Gantt chart comparing the planning results and (**b**) is the cumulative tracking score of the value function.  
Figure 15: The planning results of tracking trajectory 3: (**a**) is a Gantt chart comparing the planning results and (**b**) is the cumulative tracking score of the value function.