Reinforcement learning （RL） performance in beyond-visual-range （BVR） air combat is constrained by inadequate training opponents. This paper proposes a rule-based agent decision framework serving as RL training adversaries， where simulations confirm significantly enhanced combat effectiveness through efficient mastery of tactical maneuvers and improved adaptive decision-making. Fundamental aircraft maneuvers are modeled within an air combat simulation environment with collaborative strategy training modules. To address incomplete coverage and complexity in conventional rule-based decision trees， a state-machine-driven framework implements event-condition mechanisms for state transitions and combat decisions， demonstrating superior performance in comparative simulations. Finally， RL agents trained against this state-machine-based opponent under expert knowledge guidance autonomously acquire classical maneuvers while exhibiting advanced decision adaptability， providing foundational insights for BVR decision systems.

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.

Kinetic energy interceptor （KEI） is mainly used to intercept medium-range and intercontinental ballistic missiles flying in the boost phase， ascent phase and midcourse， with the characteristics of high speed and high acceleration. Based on the research and analysis of literature and modeling simulation， this paper conducts reverse design and research on KEI missile's overall parameters， aerodynamic parameters and dynamic parameters， and simulates KEI missile's flight performance and interception performance. The results show that KEI missile can accelerate to 6 km/s in about 60 s. It also has the ability to intercept ballistic missiles in the boost phase/ascent phase for typical targets， and has reference significance for the development and research of domestic interceptor weapons.

Launch flight reliability is an important tactical index of missile. Considering that the flight test data cannot meet the high reliability requirement of missile， this paper proposes a missile reliability evaluation method based on reduced factor Bayes theory， by integrating the ground data. This method first converts the ground test data and the consistency check is carried out； then， using L-M method to convert ground data into prior information of missile flight test. Finally， the lower limit of missile launch flight reliability is evaluated based on the reduced factor Bayes theory. The results of a typical case study show that the proposed method can make full use of reliability evaluation data of the missile and its subsystem， which not only reduces the cost and risk of flight test， but also increases the rationality of the missile launch flight reliability evaluation.

To address the drag acceleration profile design challenge during the gliding trajectory guidance of hypersonic vehicles， this paper proposes an improved method for designing the drag acceleration profile. To enhance the gliding range of the aircraft and improve flight state parameters， the paper designs a novel drag acceleration profile in the form of a quadratic function with the reciprocal of energy as the independent variable. The profile shape is adjusted by changing the drag acceleration values at the midpoint， satisfying various constraints of hypersonic flight. Simulation results indicate that， in comparison to the traditional quadratic drag acceleration profile， the proposed profile significantly increases the maximum gliding distance and improves flight state parameters， enabling the aircraft to complete flying missions around no-fly zones.

With the rapid development of the global military， missiles play an increasingly important role in modern war. The realization of precise guidance for missiles has become an important problem to be solved at present， and the optical seeker is an important equipment to realize precise guidance. The traditional gimbal optical seeker has many disadvantages such as large volume， high cost， and complex structure， so the strapdown optical seeker appears. Although the strapdown optical seeker can effectively solve the problems above， its detector is fixed on the missile. In addition， there are problems such as high noise， attitude coupling， and phase matching， and the strapdown optical seeker requires high filtering/estimation algorithms. In this paper， the principle， composition， and application background of strapdown optical seekers are introduced， combed， and summarized. Through the simulation of the estimation effect of adaptive unscented Kalman filter and unscented Kalman filter， it is found that the adaptive unscented Kalman filter has higher accuracy. Finally， the future development in this field is prospected.

Driven by the technological advancements and military demands， unmanned aerial vehicle （UAV） swarm warfare has emerged as a disruptive new style of warfare that can change the rules of engagement. Fierce competition has unfolded among major military powers around "swarm technologies and tactics". The concept and advantages of UAV swarm operation are introduced， and the development of the swarm operation types abroad is summarized. Based on the shortages and restrictions of conventional military equipment， the anti-swarm operation and explores the development direction of anti-swarm equipment and technology is analyzed. A dynamic kill chain construction strategy is put forward to solve the problem of inadequate application of adequate anti-swarm approaches and theories， and achieve systematic confrontation by comprehensively using a variety of anti-UAV swarm equipment in the future，laying the solid foundation for the development of the systematized anti-swarm operation.

In order to improve the support efficiency of spare parts for training equipment of space test forces， based on the idea of aerospace civil-military integration， an inventory model of spare parts for training equipment of space test forces based on information sharing and cross-level direct supply is proposed， and the existing spare parts inventory process is optimized to some extent. General to both is regarded as a system for the overall modeling， publishes a win-win goal， combined with the case fusion system dynamics simulation method，the current inventory model and the model proposed in this paper are simulated and analyzed. The results show that the inventory model established in this paper can effectively reduce the delay time of spare parts support and reduce the excessive production and storage of suppliers.It can achieve a win-win situation for both military and civilian in terms of improving spare parts support efficiency and reducing economic cost， and provide reference for improving spare parts support capability of training equipment of space test forces and promoting training level.

With the continuous development of electronic information technology， the integration and complexity of radar products are increasing， and the materials delivered by the product are difficult to guide the user to troubleshoot the product failures comprehensively and effectively. Furthermore， in the process of product usage， a large amount of fault data have not been effectively utilized. The data-based rapid troubleshooting technology for radar products uses a large amount of usage data of radar products as the main input， using hierarchical reasoning to aggregate search technology models， starting with the hierarchical structure of radar products， analyzing the relationship between functional failures and system failures， and using the propagation path of the directed graph to form a fault correlation matrix. Based on the fault correlation matrix， the technology infers effective troubleshooting strategies through the input of fault phenomena and other data， and forms guidance documents in the form of troubleshooting guides to guide users to quickly locate faults， solve problems， and improve uses’ own troubleshooting capability.