# Reinforcement Learning-Based Anti-Jamming in Networked UAV Radar Systems
## 3 System Model  
### Modeling of Radar Unmanned Aerial Vehicle (UAV) Swarm Networking  
Spectrum resource allocation is a common problem in radar countermeasures. In the actual scenario, the spectrum resources are very limited because there are numerous electronic devices working at various modes. The setting of parameters in a radar system, such as position and antenna, needs to be tuned to avoid jamming effectively. Traditional ground-based radar has the advantages of high power, long detection distance and narrow beam width. However, its mobility poses a threat for real-time detection in case of jamming. In comparison, small array radars carried by networked UAVs may realize swift position shift, phased array element combination, relatively flexible beam scanning and conversion between multiple detecting modes. Not only can UAVs in one single swarm cooperate with each other to synthesize beams, but also cooperate among multiple swarms. Thus, many parameters of targets can be observed simultaneously, which greatly improves the detection efficiency of the radar system. However, due to the energy constraints, the power and moving range of UAVs should be considered for effective applications. When multiple swarms work at the same time, each swarm needs to send the data back to the control center. The multi-channel data can be analyzed synthetically by the control center to obtain multi-dimensional information. In case the UAV performs tasks in remote areas, communication UAV may serve as a mobile relay to assist the data transmission. As an effective radar countermeasure, jamming systems may be used to decrease the efficiency of the radar system, where both the UAV swarm and the relay may be jammed. Against the above background, a radar countermeasure system composed of multi-agents is established in this paper. The system framework is shown in Figure 1a. The system includes 5 kinds of agents: jammer, radar UAV swarms, communication UAV, base station and target. Among them, radar UAV swarms, communication UAV and base station form the radar systems together. The objective of is to maximize the target information acquisition in different networking modes by adjusting the parameters of the radar system. In Figure 1b, the internal schematic diagram of a radar UAV swarm is given. A radar UAV swarm consists of a number of UAVs, and each UAV carries an antenna array. Radar UAV swarms are responsible for transmitting electromagnetic waves to detect targets and receiving signals. This process is called the transmission link of the radar UAV swarm. It is assumed that the distance between UAVs is much smaller than the distance from the whole UAV swarm to the target. Specifically, the UAVs in a swarm can form a uniform linear array (ULA) or uniform plane array (UPA) by changing their arrangement to synthesize beams. However, due to the limited array volume of the UAV, its phase shifter cannot have a high coding bit like a large ground-based phased array radar. Therefore, this paper explores the beam synthesis method under low-bit phase coding, which will be introduced in Section 3.2. The UAVs in a swarm transmit linear frequency modulation (LFM) signals with the same waveform parameters. Therefore, the UAVs in a swarm are strictly synchronized, where each UAV swarm can play a similar role to a complete array radar. In order to simplify the signal modeling process, we set up two UAV swarms in frequency division multiplexing mode. When transmitting signals, the frequency bands occupied by two radar UAV swarms only need to be expressed by carrier frequency \(f_{c}\) and bandwidth \(b\). Radar UAV swarm 1 occupies the frequency range of \([f_{c}-b/2,f_{c})\), and radar UAV swarm 2 occupies the frequency range of \((f_{c},f_{c}+b/2]\). When the mission is determined, the position of the UAV swarm remains unchanged, and the transmitting frequency, forwarding frequency and antenna pattern are tunable.