## 5 The Simulation Validation and Results Analysis
### Comparative Simulations for Communication Optimization
#### 5.1.1 Group A: With Communication Optimization  
Figures 6-8 show the trajectories at 30 s, 64 s, and 100 s, respectively. In these figures, the communication links are denoted by cyan thin dashed lines. The numbers represent the successful transmission probabilities of the links. The arrows denote the movement directions of the UAVs. It can be seen from Figure 6, that at the beginning of the simulation (\(t=30\) s), \(A_{1}\) is closer to the base station and \(A_{2}\) is farther away from the base station, thus \(A_{1}\) orbits near the base station to act as a relay for maintaining connectivity between \(A_{2}\) and the base station \(B\). At this time, \(A_{2}\) is tracking target \(T\), and sending its local estimation results back to base station \(B\) through the relay \(A_{1}\). With the movement of the target, the distance between the target \(T\) and the base station \(B\) gradually is reduced. Since \(A_{1}\) is orbiting near the base station, the distance between \(T\) and \(A_{1}\) also gradually is decreased causing \(A_{1}\) to gradually move up and track the target \(T\). When \(t=64\) s, as shown in Figure 7, the topology of the communication network is changed and \(A_{2}\) acts as a relay for \(A_{1}\). As the target moves away from the base station, for example at \(t=100\) s as shown in Figure 8, \(A_{2}\) moves near the base station to continuously act as the relay for \(A_{1}\), and \(A_{1}\) orbits near the target \(T\) to track it.  
Figure 9 shows that the successful transmission probabilities of \(A_{1}\) and \(A_{2}\). It can be seen that \(A_{1}\) acts as the relay for \(A_{2}\) during the time period [0, 64] s, and \(A_{2}\) acts as the relay for \(A_{1}\) during the time period [64, 100] s. In addition, it can be seen that, when communication optimization is considered, the average transmission probabilities of \(A_{1}\) and \(A_{2}\) to base station are 0.9076 and 0.8504, respectively. However, if \(A_{1}\) and \(A_{2}\) communicate with the base station directly without any relay, their average transmission probabilities are 0.8627 and 0.7730, respectively. Thus, the communication performances of \(A_{1}\) and \(A_{2}\) are improved by our algorithm with considering the communication optimization.  
Figure 8: Trajectories of UAVs and target at \(t=100\) s (Group A in Scenario 1).  
Figure 7: Trajectories of UAVs and target at \(t=64\) s (Group A in Scenario 1).  
Figure 9: The probabilities of successful transmission of \(A_{1}\) and \(A_{2}\) to base station (Group A in Scenario 1).  
The track of the FIM of \(A_{1}\), \(A_{2}\) and base station is shown in Figure 10. Due to considering communication optimization, the probabilities of a successful transmission of \(A_{1}\) and \(A_{2}\) to the base station are almost larger than 0.7 in Group A. Thus, \(A_{1}\) and \(A_{2}\) can transmit their measurement packets back to the base station, which can significantly enhance the global estimate performance and then improve the effectiveness of multi-UAVs cooperative target tracking.  
Figure 11 shows the distance between \(A_{1}\), \(A_{2}\). The distance is always larger than the safety distance (\(d_{\text{safe}}\) = 100 m), thus the safety of the UAVs is guaranteed.