To overcome the difficulty in intercepting large maneuvering targets， a trajectory planning method based on adaptive energy allocation is proposed for the boost phase interceptors. According to the generalized nominal effort miss guidance， the burnout parameters for each stage are optimized and the fire tables with different energies are established. Before launching， an energy-reserved fire table is used for trajectory planning， and some energy is reserved to cope with the unknown maneuvers of the target in the future. Once the target's maneuvers have been identified， a more energy-efficient fire table is selected to re-perform trajectory planning based on the remaining flight time of the interceptor in boost phase， so that the reserved energy could be gradually released. The simulation results show a significant improvement in the effectiveness of intercepting large maneuvering targets.

With the development of intelligent weapons,the traditional training methods could notmeet the demands of large-scale modern warfare.In the past decade,artificial intelligence (AI) methods such as deep reinforcement learning have made great breakthroughs in chess and electronic competitive games.It proves that the AI methods have great advantages in solving large searching space problems.Furthermore,the problems of situation prediction and temporary adjustment could be solved more effectively by AI methods.A new method for modeling of air target threat is proposed based on the research of deep reinforcement learning.The parallel scene modeling technology and the air target behavior modeling technology are used to construct the model of deep reinforcement learning.The convergence penetration strategy is calculated with iterative learning under the scene of single airplane.The successful attempt verifies that the practicability of deep reinforcement learning in modeling of air target threat.It provides support for the further research on the modeling of fleet joint air defense.