# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 5 Simulation Results and Analysis  
The simulation parameters were set as follows: The target initial position was (\(-800\) m, \(0\) m); the target moving speed was \(10\) m/s; the heading was \(20^{\circ}\); the standard deviation of the process noise was \(0.1\) m/s\({}^{2}\); the speed of the UAV was \(50\) m/s; the standard deviation of ranging sensor was \(2\) m; the standard deviation of direction sensor was \(0.05\) rad; the angular velocity range was \(-30\)-\(30^{\circ}\)/s; the safety distance between UAVs was \(50\) m; the horizon length taken in this paper was \(L=3\); and the simulation time was \(100\) s. The relevant parameters of the communication link model in Equations (7) and (8) are shown in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unit** \\ \hline \(P_{j}\) & 30 & dBm \\ \(C_{ji}\) & 1 & - \\ \(n_{p}\) & 3 & - \\ \(\gamma\): Threshold value of SNR & 10 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The relevant parameters of the communication link model. SNR: signal-to-noise ratio. The control decision of UAVs can be made using Equation (54) to obtain the control input for each UAV. In this paper, we used receding horizon optimization to solve the problem. The simulation results are shown in Figures 4-12. Figure 4 shows the trajectories of the target and the UAVs in one of the simulations. The initial position and the trajectory of UAVs are shown. The red solid line was the target's trajectory and the other lines were the UAVs' trajectory. The hollow dot and triangle represent the initial positions while the filled dot and triangle represent the final positions. We can see that at the beginning of the simulation, the UAVs got close to the target to get a better observation and then circled around it. The error of estimation is shown in Figure 5.  
Figure 5 shows the average estimate error of the UAVs. At the beginning of the simulation, the error was large and unstable, because during this time the UAVs were too far from the target. Once they caught up the target and circled around it, the estimate error dropped quickly and became stable. Figures 6-8 show the performance of three different methods for estimating the target. Figure 6 shows the situation when the received signal power was only attenuated as a function of the distance between two nodes. Figure 7 shows the result in fading environment and Figure 8 shows the performance when there existed interference between UAVs. In each condition of Figures 6-8, we ran Monte-Carlo simulations 100 times for each model. Compared with the communication probability model, it turned out that our proposed method in this paper had a pretty good performance. Figure 4: Target tracking trajectory with considering the communication noise between Unmanned Aerial Vehicles (UAVs). Figure 5: The average estimate error of the UAVs. Figure 6: The average estimate error compared with other methods in the presence of distance-dependent path loss. The results shown in Figure 9 are the average distance between UAVs with different methods. The safety distance was 50 m. We can see that the method in this paper had the shortest distance, which means the communication with our method was better than others. Figure 8: The average estimate error compared with other methods in presence of interference. Figure 7: The average estimate error compared with other methods in fading environment. Figure 10 shows the effect of UAV numbers on estimate error. We can see that as the number of UAV increased, the average estimate error decreased. When the number of UAVs was larger than 6, we ran Monte-Carlo simulation 100 times and found that the estimated error was very small and close with different methods, which can be seen in Figure 11 (the number of UAVs was 8). In Figure 11, we can see that at the beginning of the simulation, the estimation error of our method was higher than others. According to Equation (32), communication noise was treated as part of the observation noise.