随着防空反导拦截技术和装备的发展，进攻飞行器面临被防御武器拦截导致的战场生存率低、效能差等问题。针对拦截器拦截场景下的飞行器智能攻防博弈对抗问题，研究提出了一种考虑拦截器探测能力限制的飞行器智能机动突防制导策略。首先定义了拦截器视线角和探测范围，并利用拦截器与进攻飞行器之间的相对运动关系来描述攻防双方对抗态势的演变，进而基于深度强化学习原理设计了近端策略优化制导方法，构建了引导飞行器主动摆脱拦截器探测的马尔可夫决策链，并进一步优化飞行器奖励函数设计方法实现对目标的精确打击。在此基础上，通过引入动作探索和广义优势函数来解决智能算法收敛慢的问题。仿真结果表明，智能机动突防制导策略赋予了飞行器自主学习优化属性，可以通过主动规避机动增加拦截器的探测难度，最终突破拦截器的探测能力极限实现突防逃逸。相比于传统PN-sin突防制导方法，本文研究提出的突防制导策略能够在攻防双方非对称机动能力场景下保持更高的突防成功率。

In the face of the development of air defense and missile defense interception technologies and equipment, offensive aircraft are confronted with issues such as low battlefield survivability and poor effectiveness due to the interception by defensive weapons. Addressing the intelligent offensive and defensive game confrontation problem of aircraft under the interception scenario, this research proposes an intelligent maneuvering penetration guidance strategy for aircraft that considers the limitations of the interceptor's detection capabilities. Initially, the interceptor's line-of-sight angle and detection range are defined, and the evolution of the confrontation situation between the interceptor and the offensive aircraft is described using the relative motion relationship between them. Subsequently, a near-end policy optimization guidance method is designed based on the principles of deep reinforcement learning, constructing a Markov decision chain that guides the aircraft to actively evade the interceptor's detection, and further optimizing the aircraft's reward function design method to achieve precise targeting. On this basis, the convergence speed of the intelligent algorithm is addressed by introducing action exploration and generalized advantage functions. Simulation results show that the intelligent maneuvering penetration guidance strategy endows the aircraft with autonomous learning and optimization attributes, allowing it to increase the difficulty of detection for the interceptor through active evasive maneuvers, ultimately breaking through the detection capability limit of the interceptor to achieve penetration and escape. Compared with the traditional PN-sin penetration guidance method, the penetration guidance strategy proposed in this paper can maintain a higher penetration success rate in scenarios where the offensive and defensive sides have asymmetric maneuvering capabilities.