# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 4 Numerical Results
### The First Set of Parameters
Other conditions were consistent with the experimental group. Figure 4a shows the trend of MSE for the target location estimation when the time slot range was 0-100. It can be seen that at the beginning of the iteration, the MSE curves corresponding to the experimental group, control group 1, and control group 4 decreased significantly faster than the MSE curves corresponding to the control group 2 and control group 3. Based on Figure 4b, it can be seen that, at the beginning of the iteration, the experimental group showed better performance than control group 1 ([PERSON]'s method [31; 33]): For example, in the 10 th time slot, the MSE generated by the experimental group was about 440 m lower than that of control group 1. In the 20 th time slot, the MSE generated by the experimental group was approximately 270 m lower than that of control group 1. This fully proved the effectiveness of the JRCO algorithm, which achieved a better performance in target localization than the current research of [PERSON] et al. The JRCO algorithm adopted by the experimental group achieved a good target localization performance by fully utilizing the degrees of freedom in radar communication systems. The MSE curves of the experimental group, control group 2, and control group 3 were compared, as shown in Figure 4a. It was obvious that the MSE of the experimental group decreased the fastest overtime at this time slot scale. Control group 2 applied a centralized algorithm, but its corresponding MSE values showed significant fluctuations. This was because the same step size across all the time slots led to the poorer convergence performance of the JRCO algorithm. This resulted in no convergence trend in the curve of control group 2, and the iterative efficiency of the algorithm in this group was relatively low. Although control group 3 also adopted a centralized algorithm, the location of only one UAV was optimized in each time slot. This greatly reduced the iterative efficiency of the algorithm. Finally, the MSE curves corresponding to the experimental group and control group 4 were compared. It could be concluded that these three curves were relatively close on the scale of 0-100 time slots. Therefore, Figure 4b,c provide the MSE curves in the range of 10-20 time slots and the 100 th time slot, respectively. From the condition settings of the five simulation experiments, it could be seen that the optimization algorithm applied in control group 4 could provide the best performance (i.e., the corresponding MSE curve decreased the fastest over time). For Figure 4c, it could be seen that at the end of the algorithm, the MSE curves of control group 4 and control group 1 were actually closer, and the experimental group obtained the lowest MSE. The MSE of the experimental group (5.66 m) decreased by approximately 60.99% (8.85 m) compared to the MSE of the control group 4 (14.51 m). This fully demonstrated the potential and advantages of the proposed JRCO algorithm. To demonstrate other details generated by the joint optimization algorithm, Figure 4d provides the initial and final locations of the UAV swarm. Figure 4e shows the average operation time of each time slot as a function of the number of UAVs (only the results on the \(xOy\) two-dimensional plane were plotted).