# A Low Complexity Persistent Reconnaissance Algorithm for FANET
## 4 Results
### Detailed Comparison of Algorithms  
In this section, we examine the four algorithms from a variety of perspectives, including the overall performance, average threat value for UAVs, longest and shortest link distances between UAVs, and algorithm complexity. In order to ensure the effectiveness of comparative experiments, all parameters in different algorithm experiments are consistent. The results are averaged over 20 repeated experiments. Due to the randomness of RWP, its total performance (Equation (11)) is not ideal and the value varies rapidly. Thus, Figure 10 shows the overall performance variations of PSO, TARF, and TA&CRFO as the number of available RUs increases. We notice that the connectivity value of these three algorithms is between 34 and 50, after which they tend to be stable, while the FANET threat metric is in the range of 8-12. To ensure the fluctuation of these two values is consistent, the weight factors \(w^{C}\) and \(w^{T}\) were set to 0.5 and 2.5, respectively. The network's connectivity requires a certain number of UAVs as a guarantee, so the number of RUs starts from seven, according to the simulation experiment. It can be seen that with the increase in RUs, the total performance of the three algorithms becomes larger. However, it should be noted that the performance improvement of PSO mainly comes from network connectivity, so its growth trend gradually decreases. Whereas the performance of TARF and TA&CRFO is first improved due to network connectivity; then, as the RUs' option space increases, low-threat density areas are selected as relay positions. Figure 11 presents the UAV's average threat value obtained by constructing the FANET with different algorithms. It is worth mentioning that most places in the scene have a threat PDF between 6 and 13. It can be easily seen that the UAV's average threat value in RWP and PSO does not decrease with the increase in RU number but fluctuates randomly. This is because RWP moves randomly in space, while PSO only cares about the interconnection between nodes and does not consider the threat PDF information in the scene. On the contrary, the UAV's average threat value in TARF and TA&CRFO is lower than that of the above two algorithms and gradually decreases with the increase in relays. Among them, TARF is slightly better than TA&CRFO, mainly because TA&CRFO performs distributed adjustments sometimes and cannot keep the optimal global state at all times. Figure 10: Total Performance of algorithms. Figures 12 and 13 show the longest and shortest UAV distance of the FANET constructed by different algorithms. Taken these two metrics together, they demonstrate FANET's compactness and uniformity. As shown in the figures, other algorithms can meet the constraints of the maximum communication distance of 200 m between UAVs and the minimum safe distance of 50 m, except RWP. Among them, with the increase in relays, the decrease in PSO's longest link distance is more significant than that of TARFC and TA&CRFO, while the decline of the shortest UAV distance is smaller than that of TARFC and TA&CRFO. It can be seen that PSO can make the FANET's nodes tend to be evenly distributed. However, for realistic scenarios with uneven threat PDF, algorithms such as TARFC and TA&CRFO obviously have more advantages since they can bypass the high-risk area. Figure 11: UAV’s average threat value with different algorithms. Figure 12: The longest link distance of FANET constructed by different algorithms. We assume that the high-altitude UAV control center has superior operational capability and can obtain the algorithm's optimization results in milliseconds. However, the FANET results constructed by the algorithm cannot be provided in time due to the limitations of our simulation equipment. To visualize the algorithms' complexity, we use the same parameter settings and scene settings to simulate two detected targets at different positions moving 300 m in a straight line.