# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 5 Simulation Results
### Collaborative Scattering Point Localization  
In this scenario, the radar needs to locate a strong scattering point on the target. Let radar UAV swarm 1 measure angle and radar UAV swarm 2 measure range, because we assume that only radar UAV swarm 2 has the ability to generate narrow beams of angle measurement. When measuring distance, the target is regarded as a whole, which is the same case in collaborative target ranging. When measuring the angle, the target is regarded as 5 discrete scattering points. The strong scattering points are then located according to distance and angle. The angle measurement adopts a beam scanning method. At this time, two UAV swarms need to be in close positions. In order to highlight strong scattering points, 5 scattering points of the target are set to different intensities. The intensity of scattering point 3 in Figure 7 is slightly stronger than that of the other four scattering points. In this way, the angle measurement is concretized as the angle tracking of a key point on a wide azimuth target by radar UAV swarm 2.  
The default policy of this experiment is the same as that of collaborative target ranging, except that the width of two mainlobes are \(50^{\lx@math@degree}\) and \(10^{\lx@math@degree}\), respectively. This is because, for the default policy of angle measuring radar, its mainlobe is relatively narrow.  
In particular, since the two radar UAV swarms are relatively close at this time, we use the same point to represent them in Figure 13. At this time, the antenna pattern of UAV swarm 2 shown in Figure 13 does not mean that the beam always points to scattering point 3. It represents the mainlobe width when the beam of UAV swarm 2 sweeps through scattering point 3 instead.  
Figure 12: Normalized information chart of collaborative target ranging.  
In Figure 13, the mainlobe of the angle measuring UAV swarm (UAV swarm 2) is very narrow, only \(5^{\circ}\). This is because for the beam scanning method, the narrower the mainlobe width, the larger the mainlobe  
the angle measuring information. At this point, the policy of the angle measuring UAV swarm no longer pursues the high SIR, but seeks a balance between the SIR and the mainlobe width. In this way, the angle-measuring UAV swarm realizes angle tracking of the key part (scattering point No. 3) of the target.  
Figure 14 also proves that the frequency policy of the radar UAV swarm and the communication UAV can still be correctly generated when the ranging and the angle measuring modes coexist. Figure 15 shows the effectiveness of DGQL. In particular, we show the result of the policy based on the reward generated by SIR only. At this time, radar UAV swarm 2 adopts a mainlobe width of \(20^{\circ}\). Although it covers more scattering points and gets higher SIR. The information quantity is still slightly lower than the DGQL policy. Wide mainlobe policy reduces the accuracy of radar angle measurement, which fully proves the validity of information representation theory. Figure 16 also proves that in the scenario of collaborative scattering point localization, increasing the number of action combinations does not significantly improve the information acquisition ability of the radar.