In recent years， unmanned aerial vehicles have become an indispensable and important weapon in modern warfare， and the offensive and defensive confrontation system covering unmanned aerial vehicle systems has become one of the basic characteristics of modern warfare. Studying the survivability problem of unmanned aerial vehicle systems under strong denial conditions has important application value for future battlefield confrontation. This paper analyzed the challenges faced by combat unmanned aerial vehicle systems in the physical， electromagnetic， and information domains and gave targeted strategies to cope with different denial conditions. The combat scene， combat configuration， and movement tactics were proposed for unmanned aerial vehicle systems under strong denial conditions. The key problems related to survivability were studied， including the bifocus random walk algorithm of relay swarm， the secondary planning technology of denial space path， and the service-oriented architecture technology of intelligent edge accusation center. By optimizing the overall design of the combat system， the survivability of unmanned aerial vehicle systems under strong denial conditions can be effectively improved to cope with the complex combat environment of future battlefields.

To address the issues of low search efficiency， poor path smoothness， and limited local obstacle avoidance in traditional A* algorithms within complex 3D environments， this paper proposed a hierarchical fusion algorithm combining A* and DWA. The algorithm first extended the A* algorithm into 3D space using a 26-neighbor node search strategy. It introduced a dynamic adjustment term into the cost evaluation function to achieve adaptive weighting， and employed the Douglas-Peucker algorithm combined with cubic B-spline curves to smooth paths. Second， it integrated a 3D-extended DWA algorithm to compensate for A*'s local obstacle avoidance limitations. A 3D dynamic window model was constructed via kinematic decoupling， and a cosine similarity measure was introduced to enhance the evaluation function， thereby improving real-time obstacle avoidance performance. Finally， a dynamic feedback mechanism was designed to enable adaptive correction of the global path， forming a closed-loop optimization system： “A* global planning -DWA local obstacle avoidance-Dynamic feedback.” Simulation results demonstrated that in both static and dynamic 3D environments， the A*-DWA hierarchical fusion algorithm significantly outperformed other comparison algorithms in path length， planning time， and path smoothness. Obstacle avoidance success rates exceeded 90% across multiple scenarios， validating the effectiveness of the A*-DWA hierarchical fusion algorithm.

With the rapid development of frontier technologies such as artificial intelligence and big data， UAV swarm are gradually becoming an important application in the military domain. Their characteristics of low cost， high flexibility， and strong survivability make them an ideal “zero-casualty” weapon in modern warfare， especially in high-risk operations like amphibious assault. This paper first reviewed the research progress of UAV swarm technology in China and abroad， analyzing the development and applications by various military forces in this field. It then systematically summarized the key factors influencing amphibious assault operations， such as defensive defenses， climate and terrain， operational tempo， and fire support. Subsequently， the advantages of UAV swarm in reconnaissance and assessment， deception and jamming， firepower strike， communication relay， and logistical support were elaborated， leading to the construction of a UAV swarm operational system. Finally， considering the complex environment of amphibious assaults， the paper proposed development recommendations for UAV swarm technology in areas such as autonomous coordination， intelligent decision-making， network confrontation， and sustained operational capabilities， providing a reference for innovative applications of UAV swarm technology in the future.

The working principle of αβ filter is studied， and the functional expressions of the filter in the discrete and continuous domains are derived. Based on the transfer function， stability analysis of the filter is carried out， and combined with the engineering design， the range of α and β parameter values that make the filter system stability are achieved. The commonly used parameter design methods are analyzed， the current problems of the filter are pointed out， and an improved adaptive αβ filter method is proposed. Finally， the influences of parameters α and β are analyzed through simulation， and the filtering performance of the improved adaptive filtering method and the traditional filtering method are compared. The simulation results showed that the selection of parameters α and β have a significant impact on the filtering effect. What’s more， the improved adaptive filtering approach has better filtering performance than traditional method， with smoother filtered data and better filtering effect on abnormal noise.

To optimize the operation performance of the command space under augmented reality， the multi-screen linkage layout design research was carried out. Starting from the current situation of command space information layout design and emergency decision-making needs， the sub-objective function was established by applying event tracking data analytics and the fuzzy analytic hierarchy process， and the overall layout of 16 functional areas was obtained through K-Means clustering. The differences among the three sets of design schemes were tested and compared. According to the principle of importance priority， placing interactive components near the visual center is better than prioritizing metaphor. Placing target locking controls at the bottom right of their functional areas is better than at the top left. Increasing the display area of map components can improve system usability.

To address the high algorithm complexity and low timeliness in existing circular array interferometer direction finding methods for building systems， this paper proposed an interferometer direction finding method suitable for system implementation， which was then used in actual systems. First， this method first improved the conventional calculation method for the range of fuzzy numbers based on the amplitude comparison direction finding （ACDF） results， thereby reducing computational complexity and improving processing speed. By considering that higher frequencies enhance ACDF accuracy under the same conditions， the method dynamically segmented the amplitude comparison range in real time by integrating the frequency information and the ACDF results. Then， by taking advantage of the fact that the fuzzy multi-valued differences of each baseline are relatively large， while the true angle has a unique property， the ambiguity resolution for the circular array interferometer direction finding was achieved. Finally， based on the proposed method principle， a miniaturized eight-element uniform circular array interferometer broadband receiver was implemented and designed using an FPGA. Both simulations and field experiments verified that the system achieves high-precision instantaneous direction finding for a radiation source target， with an accuracy of up to 1.5°， demonstrating strong practical value for engineering applications.

In light of the increasingly multidimensional and complex combat environment， the form of warfare has also undergone a profound transformation. With the continuous development of artificial intelligence technology， its influence in various fields keeps growing. This paper provided a comprehensive overview of the principles and evolution of reinforcement learning， and analyzed the application of deep reinforcement learning in the field of intelligent guidance. It further summarized key technologies within intelligent guidance， and based on current research progress， offered a detailed examination of existing challenges and the implications of AI. This analysis aims to provide insights and directions for the advancement of intelligent guidance.

This study designed an optimal control algorithm based on linear quadratic regulator （LQR） and proposed the integration of output residual integral control to improve the performance of the optimal controller. A controllable and observable model of the servo system was built under friction-neglected conditions. Based on the LQR optimal control theory， the optimal control law for the system was derived. The influence of friction on the controller's performance was studied， and integral control was incorporated into the optimal control loop to improve steady-stage performance. Experimental results show that， compared with the PID lead-lag compensator， the proposed optimal controller exhibits zero overshoot during the presetting stage， reduces settling time by 47%， decreases steady-stage response delay by more than 55% while lowering the decoupling rate by over 50%. With the addition of output residual integral control， the issues of overshoot and tailing caused by friction in the steady stage are effectively eliminated， resulting in a decoupling rate below 4% and a further delay reduction exceeding 40%. Vibration tests indicate that the designed optimal controller possesses both stability and robustness， thereby laying a foundation for the future engineering application of the optimal control method in product models.

In the process of multi-sensor target information fusion， a consistent distributed information filtering algorithm is proposed to improve the high dependence and low stability of the centralized filtering algorithm on the fusion center in the target tracking state estimation.The algorithm is based on the state information and measurement information of each sensor itself and neighbor nodes to achieve real-time tracking and positioning of the target， and each node is a data processing center， which solves the problem of insufficient anti-damage capacity of the fusion center and ensures that when some sensor nodes have problems， the global task can be carried out smoothly. Simulation analysis shows that the consistency distributed filtering algorithm can solve the problem of multi-sensor target tracking information fusion， and its accuracy is higher than that of a single sensor， and tends to be the accuracy of multi-sensor centralized information filtering， which can be applied to nonlinear system tracking with high stability and reliability.

To address the issue of weak disturbance rejection and poor control accuracy in two-dimensional trajectory correction fuze-caused by the axial friction torque and the external aerodynamic disturbances during projectile flight-this study proposed a roll angle control method based on a fixed-time sliding mode control. This method ensured that the system converged within a fixed time， independent of its initial state. The stability of the designed control system was analyzed by using some relevant theories of the fixed-time convergence. Comparative numerical simulations with existing algorithms demonstrate that the proposed fixed-time control method enables both the roll angle and the angular velocity of the fuze to respond rapidly and accurately within a prescribed time. It also improves the system's control precision， response speed， and robustness.

To address the low accuracy of collaborative recognition caused by the difficulty of feature extraction in active and passive data collaborative recognition， this paper proposes an active and passive data collaborative recognition algorithm based on dual-channel convolution and an attention mechanism. The active and passive data are associated and fused， and then a dual-channel convolutional network is used to extract data features. On one channel， two large convolution kernels are employed to capture low-frequency features. Larger convolution kernels can enhance robustness to noise. On the other channel， small convolution kernels are used to enhance the neural network’s ability to extract detailed features. Meanwhile， an attention mechanism is used to enhance the network’s ability to extract key features， and a bidirectional LSTM network is added to extract complex temporal features. Experimental results show that the proposed method can effectively improve recognition accuracy and demonstrates strong practical applicability.

In response to the increasing importance of underground space operations in future urban warfare， to address the current issues of insufficient research on underground space operations and unclear capability requirements， a grey quality function deployment （GQFD） method based on grey correlation analysis is adopted. After fully studying and analyzing the concept of urban underground seizure and control operations and the basic “task-capability” requirements， a grey correlation analysis and a house of quality demand model are established. Through analysis and calculation， the key capability requirement indicators and their importance ranking for urban underground seizure and control operations are obtained， providing a certain basis for the research on urban underground warfare and the demonstration and construction of equipment requirements.

This paper proposed a sliding mode cooperative terminal guidance law to solve the problem of cooperative attack of the leader-follower missiles against a maneuvering target. The sliding mode surface was designed by taking the difference of the distance between the leader and the follower and its differentiation as the cooperative state variables， and the estimated time-to-go of the leader and the line of sight rate of the follower were introduced， so as to avoid the singularity problem. The target’s maneuvering acceleration was treated as a disturbance variable， and a switching control term utilizing its bounded nature was designed， ensuring the stability of the system and enhancing the robustness of the guidance law. Finally， digital simulations were conducted with three conditions. The results show that the guidance law proposed in this paper can achieve cooperative guidance.

To address the issues of weight allocation for parameters affecting the equipment health and the fusion of evaluation values for each parameter， a health assessment method of equipment based on technique for order preference by similarity to an ideal solution （TOPSIS）-rank-sum ratio （RSR） algorithm and interval-valued intuitionistic fuzzy （IVIF） was proposed according to the characteristics of equipment and its life extension requirements. Firstly， IVIF was introduced to determine the weight of decision experts， and the index weight was determined according to the evaluation hesitation degree and similarity degree， obtaining a weighted normalized matrix. Then， the TOPSIS was established， and the RSR assessment framework was integrated with TOPSIS to realize the health assessment of equipment and decision-making. Finally， simulation cases were utilized to evaluate， rank， and grade the health status of multiple equipment of the same model， and two known evaluation methods were compared to verify the feasibility of the proposed method.

Support chain is a research hotspot in the field of equipment support， lacking method support in the model construction. This paper drew on theories such as supply chain， modern logistics， systems approach and summarized the concept of aerospace equipment support chain. It analyzed the relationship between it and the related network chain information system， understood and grasped its characteristics， and promoted their integration. The paper proposed a model construction method “from business practice to abstract expression and from quantitative analysis to link optimization”， which was realized through five steps， such as “business combing， element setting， structure mapping， demand matching， and optimization and reconstruction”. It provided support for the theoretical application of the aerospace equipment support chain.

This study aims to enhance the control performance of second-order uncertain systems， especially those characterized by unknown models and accompanied by dynamic disturbances. A novel control strategy was proposed using the ball-and-plate system as the experimental object. An RBF neural network （RBF1） was used to predict the key parameters of the system， and its internal parameters were dynamically adjusted through adaptive algorithms to ensure prediction accuracy. Based on this predictive model， a sliding mode controller with an integral sliding surface was designed， enabling the system states to reach the sliding manifold directly and thereby improving the robustness and response speed. To further optimize control performance， this paper innovatively introduced a second RBF neural network （RBF2） to dynamically adjust the sliding mode controller parameters， which were tuned via the gradient descent method， enhancing the flexibility and adaptability of the control strategy. Simulation experiments show that this control strategy performs well in trajectory tracking of ball-and-plate systems， effectively dealing with system uncertainties and disturbances， which confirms its favorable control performance and practical application prospects.

To improve the coverage effect of UAV swarm deployment in random environments such as disasters and battlefields， this paper proposed a multi-UAV communication coverage deployment strategy based on adaptive virtual force particle swarm optimization. Firstly， the gravity model was introduced into the traditional particle swarm optimization algorithm to modify the fitness function of the particle swarm optimization algorithm to achieve the balance between coverage and energy consumption. Secondly， in order to reduce the communication interference between UAV， an adaptive lift control strategy based on the exclusion model is designed to reduce the repeated coverage between multiple UAV. Finally， five algorithms were selected for comparative analysis to verify the effectiveness of the proposed method. The experimental results show that， while ensuring the maximum area coverage， the repeated coverage rate of the proposed method is only 21.01%， and the average energy consumption is only 4.02×10² kJ， which is significantly lower than other comparison algorithms. In addition， the proposed method can effectively reduce communication interference and resource waste， avoid falling into local optimum， and is not limited by the number of UAV. It has the ability to deploy in an environment with random changes in the number of UAV， showing good environmental adaptability.