# Secure Beamforming Design for UAV-Empowered Integrated Sensing and Communication
## 4 Simulation Results and Performance Analysis
The figure clearly demonstrates that with an increasing sensing threshold, the maximum eavesdropping SINR rises monotonically while the performance disparity between the proposed algorithm and Algorithm 1 diminishes. Using dedicated sensing signals and optimizing the UAV position can significantly improve the system's security performance. Figure 2: Convergence of the proposed algorithm. Figure 5 illustrates the relationship between the maximum eavesdropping SINR and the UAV transmit power, where the communication threshold is 5 dB and the sensing threshold is \(-20\) dBm. The figure clearly indicates that the maximum eavesdropping SINR decreases monotonically as the UAV transmit power increases. As the UAV transmit power increases, the system, while ensuring communication and sensing performance, allocates more resources to improve the system's security performance. This causes the maximum eavesdropping SINR to decrease monotonically with increasing transmit power. Figure 4: Maximum eavesdropping SINR vs. sensing threshold. Figure 3: Maximum eavesdropping SINR vs. communication threshold. Figure 6 illustrates the correlation between the maximum eavesdropping SINR and the number of antennas, where the communication threshold is \(8\) dB, the sensing threshold is \(-18\) dBm, and the UAV transmit power is \(0.3\) W. The figure clearly demonstrates that once the number of antennas surpasses \(16\), the rate at which the maximum eavesdropping SINR decreases begins to diminish. This is because a radar system with \(16\) antennas has sufficient spatial degrees of freedom to provide adequate signal processing capability; therefore, more antennas do not significantly lower the maximum eavesdropping SINR. Furthermore, the performance gap in terms of the maximum eavesdropping SINR between the proposed algorithm and the comparison algorithms increases with the number of antennas. This is because the additional antennas provide more spatial degrees of freedom, increasing the UAV positional freedom and making the UAV position more impactful on the system's security performance. Next, we simulate the multiple eavesdroppers scenario, with \(J=2\) eavesdroppers and \(K=2\) legitimate users. Building on the single eavesdropper scenario, the second eavesdropper is located at \((300,650,0)\). The UAV is equipped with \(N=8\) antennas, and  
Figure 5: Maximum eavesdropping SINR vs. transmission power. Figure 6: Maximum eavesdropping SINR vs. number of antennas. its maximum transmit power is set to \(P_{\max}=0.25\) W. Figure 7 shows the variation of the maximum eavesdropping SINR with increasing communication thresholds for legitimate users, where the sensing threshold is \(-13\) dBm. It can be observed that the maximum eavesdropping SINR for multiple eavesdroppers is always greater than that for a single eavesdropper. This is because multiple eavesdroppers require the UAV to sense multiple targets, consuming more system resources. Additionally, multiple eavesdroppers generally exhibit better eavesdropping performance than a single eavesdropper, leading to a higher maximum eavesdropping SINR. Figure 8 shows the relationship between the maximum eavesdropping SINR and the sensing threshold, with the communication threshold set to 9 dB. As the sensing threshold increases, the maximum eavesdropping SINR increases monotonically for both single and multiple eavesdroppers. However, in both cases, the maximum eavesdropping SINR is always significantly lower than the communication SINR of the legitimate user. Figure 8: Comparison of single and multiple eavesdroppers: maximum eavesdropping SINR vs. sensing threshold. Figure 7: Comparison of single and multiple eavesdroppers: maximum eavesdropping SINR vs. communication threshold.