To deal with multi-unmanned aerial vehicle （UAV） path planning， a mathematical model was developed for UAVs and threat zones to simulate scenarios close to real-world conditions. Based on this， a dynamic scene trajectory planning algorithm based on multi-agent reinforcement learning （DSTP-MARL） was designed for intelligent path planning of multiple UAVs. This algorithm ensures that UAVs reach target destinations safely by avoiding threat zones and helps optimize mission routes. To evaluate the performance of the algorithm， DSTP-MARL was compared with the deep Q-Network （DQN）. Experimental results show that， whether in simple or complex threat environments， DSTP-MARL demonstrates superior obstacle avoidance and mission completion capabilities. In terms of convergence speed and process stability， DSTP-MARL exhibits significant advantages over DQN， converging faster and more stably， thus enhancing mission efficiency. These results indicate a higher practical value and broader application potential of DSTP-MARL.

In order to improve the identification capability of air targets and promote the efficiency of joint operations， a air target comprehensive identification method based on airspace coordination is proposed from the perspective of battlefield airspace control. The basic connotation， operational tasks and implementation methods of battlefield airspace control are introduced， and the internal relationship between air target comprehensive identification and battlefield airspace control is clarified. A brief description of the U.S. Forces air target hostile identification idea is given around airspace coordination measures， and introduces the basic concept， identification process and identification logic of air target comprehensive identification. Based on mathematical tools such as D-S evidence theory， the engineering application of air target comprehensive identification is investigated. The reliability of the method is verified by example calculations， which can provide some references for related researches.