# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 4 Numerical Results
### The First Set of Parameters  
The simulation parameters of the experimental group are shown in Table 1. The focus of parameter selection was mainly on the geometric scale between the UAV and the target. The selection of parameters was mainly based on the currently distributed radar literature, and the literature selected for this subsection was [41]. The meanings of the experimental group and the four control groups were as follows:  
Experimental group: Under the simulation parameters shown in Table 1, the joint optimization method in Algorithm 2 was applied. Control group 1 ([PERSON]'s method [31; 32; 33]): Based on the simulation parameters shown in Table 1 and the JRCO algorithm in Algorithm 2, the communication coverage range of each UAV was kept unchanged (set at 150 m), and this control algorithm only planned the location of each UAV. At this point, the radar detection power and communication transmission power of each UAV remained unchanged with time, and the other conditions remained consistent with the experimental group. At this point, the optimization method only took the estimated CRLB as the optimization objective and planned the location (or path) of the UAV. This method was the same as the method in references [31; 32; 33]; therefore, the control group aimed to compare the proposed JRCO algorithm with the current research of [PERSON] et al. Control group 2: Under the simulation parameters shown in Table 1, the JRCO algorithm in Algorithm 2 was performed, but the communication coverage range of all UAVs was infinite. The measurement error of AOA for each UAV was calculated based on the communication coverage radius of 150 m, which was equivalent to a centralized algorithm. Meanwhile, the optimal locations of all UAVs were generated through the same RPGD method. That is, all UAVs applied an equal step size in the RPGD method when executing the algorithm, and other conditions were consistent with the experimental group. Control group 3: Under the simulation parameters shown in Table 1 and based on the JRCO algorithm in Algorithm 2, the communication coverage range of all UAVs was infinite. The measurement error of the AOA for each UAV was calculated based on a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value or Range** \\ \hline The number of UAVs & \(M\) & 20 \\ The number of time slot & \(T\) & 100 \\ Policy Selection Threshold 1 & \(\varepsilon_{1}\) & 0.1 \\ Policy Selection Threshold 2 & \(\varepsilon_{2}\) & 0.45 \\ Electromagnetic domain power & \(E_{\max}\) & 1000 W \\ Maximum step size per time slot & \(\mathrm{l_{max}}\) & 30 m \\ The-coordinate range of the feasible area & \([x_{\min},x_{\max}]\) & \([0,1000]\) m \\ The-coordinate range of the feasible area & \([y_{\min},y_{\max}]\) & \([0,200]\) m \\ The height of the feasible area & \(z_{\mathrm{U}AV}\) & 50 m \\ Target location & \([x_{u},y_{u},z_{u}]\) & \([500,1000,100]\) m \\ The side length of departure area & \(b\) & 50 m \\ Collection of communication coverage & \(\rho\) & \([130,140,150,160,170]^{T}\) m \\ Variance of UAV location error & \(\sigma_{l}^{2}\) & 0.005 \\ The number of Monte Carlo experiments & \(\mathrm{N/A}\) & \(10^{6}\) \\ \(\mathrm{forcalculating}\left(\sigma_{\varphi,k}^{t}\right)^{2}\) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The first set of parameters for numerical simulation. communication coverage radius of 150 m, which was equivalent to a centralized algorithm. However, the location of only one UAV was planned in each time slot, and all UAVs were planned in a sequence of a different time slot, while the other conditions were consistent with the experimental group. Control group 4: Under the simulation parameters shown in Table 1, the JRCO algorithm in Algorithm 2 was performed, but the communication coverage range of all UAVs was infinite. The measurement error of AOA for each UAV was calculated based on the communication coverage radius of 150 m, which was equivalent to a centralized algorithm.