## 3 Construction of Simulation Model
### Efficiency Evaluation Level  
In the UAV swarm countermeasure simulation platform, the core function of the efficiency evaluation level is to judge the outcome. It is based on a series of key indicators to determine the tactical results of opposing sides. Mission completion is the primary index to judge the outcome, which measures the efficiency and success rate of UAV swarm in performing tasks such as target strike, reconnaissance, and surveillance. Survival rate is also very important. It evaluates the durability of swarms by the damage rate of UAVs and the survival time of battlefields. Resource consumption is also a factor that cannot be ignored, which relates to the efficiency of the use of key materials such as fuel and ammunition, and reflects the continuous combat capability of swarm. Reaction time is used to assess the response speed of UAV swarms to enemy threats. This directly affects the timeliness of tactics and the reduction of enemy attack opportunities, so it is also a key component of judging the outcome. In addition, the judgment of winning or losing should follow a series of preset rules and conditions, such as specific mission objectives and  
Figure 3: State space of mission UAVs.  
battlefield rules of engagement, which provide clear standards for judgment. A real-time monitoring and feedback mechanism ensures the dynamic and adaptive nature of victory and defeat judgments, allowing commanders to adjust tactics and strategies based on real-time data. In the end, judging the outcome is a process of comprehensively considering various factors. By comprehensively analyzing task completion, survival rate, resource consumption, reaction time, etc., scientific and objective evaluation results are obtained, which provide support for tactical decision-making and strategy optimization.  
The operating logic of the complete simulation system is as follows:  
Firstly, the mission level defines the mission scenario and objectives, providing initial conditions for the kinematics model level. Then, the kinematics model level generates the dynamic behavior of the UAV and target based on mission requirements, providing data for the sensor information fusion level. The sensor information fusion level processes sensor data to provide environmental perception information for the intelligent algorithm level. The intelligent algorithm level generates decision instructions based on sensor information, providing decision support for the command and control level. The command and control level generates specific control commands based on the instructions of intelligent algorithms, and feeds them back to the dynamic model level to update the behavior of the UAV. Finally, the efficiency evaluation level assesses the completion status of missions and system efficiency, providing feedback to the mission level for adjusting subsequent mission settings.  
The simulation environment is built on the local host, the software environment is compiled by python, the GPU is GeForce RTX 4070 Ti 12G, the CPU is Intel 13,900 K, and the memory is DDR5 32G. The parameter settings related to the simulation environment are shown in Table 2, where the red side refers to the mission UAV and the blue side refers to the ground target.