Intelligent command and control is the inevitable trend of future UAV cluster combat command and control. Based on object-process methodology, a conceptual model of intelligent command and control for UAV cluster is proposed in this paper. Under the unified view, the physical objects, information, environment and process involved in the intelligent command and control of UAV cluster are abstractly described. The top-level conceptual model of intelligent command and control of UAV cluster, SD1-level amplification model as well as SD2-level sub-process model is designed and constructed by means of operational-functional unified specification modeling(OFUS). The model is tested and verified by the function of OPCAT software, and the result proves the logical feasibility of the model.

The multi-agent system driven by large models has great potential in enhancing the level of artificial intelligence, providing innovative solutions for military intelligence. However, the degree of autonomy of current large model driven multi-agent systems in independently completing task objectives is greatly affected by task complexity, and the consistency between system processing results and initial objectives is poor. It is necessary to evaluate and analyze the autonomy and consistency of large model driven multi-agent systems. Previous studies have not yet comprehensively evaluated the autonomy and consistency levels of large model multi-agent systems. This article proposes a multidimensional evaluation method that can analyze and extract the autonomy and consistency of the overall architecture of multi-agent systems driven by large models, and obtain the overall performance evaluation results and specific improvement methods of the system. Through experimental analysis of 7 selected systems, the feasibility of multidimensional evaluation methods in practical applications has been verified.

The complexity of war games poses challenges for single-technique-built agents, as it requires balancing between coarse-grained strategic adjustments and fine-grained action control, thus limiting agent performance. To address this, this paper proposes a multi-level agent architecture for military strategy games based on the "Intent-Task-Action" framework, aiming to integrate various technical strengths for intelligent agent modeling in these games. Through a top-down decomposition and propagation mechanism, this architecture progressively breaks down combat strategies into executable tasks and actions. Techniques such as Finite State Machines, Coalition Games, and Behavior Tree are employed to implement the different levels of the agent architecture. The application scope of the realized agent is preliminarily defined as the tactical level scenario of the land combat unit. Finally, through multi-round adversarial experiments with various benchmark AI on a war gaming platform, the feasibility and effectiveness of the proposed architecture are validated.

According to the practical task of data construction and development, and the characteristics of data integration and application for “wide area multi-ability warfare”, a scheme of wireless intelligent transmission for large data in combat is proposed based on Mesh network technology, real-time transmission of various types of combat data to remote data processing platforms for visual operations and intelligent data storage and analysis, to provide command organizations at all levels, interactive and controllable “science and technology +”“network +” data integration using a new model.

The fuze is the critical actuating component that enables high-angle artillery ammunition to achieve its terminal effects. Its performance directly impacts the coordination between the artillery ammunition and the firing system, thereby determining the probability of destruction of aerial targets by the high-angle artillery ammunition, and to a significant extent influencing the outcome of the war. This paper analyzes the primary loading conditions experienced by the fuze of high-angle artillery ammunition within the gun barrel, and discusses the methods of performance simulation and experimental verification. The analysis and research presented in this paper can provide valuable reference and inspiration for the structural design and performance simulation of high-angle artillery ammunition fuzes in the future.

To improve the accuracy of Low altitude unmanned aerial vehicle(UAV) target detection and tracking, an improved UAV detection algorithm based on YOLOv7 and DeepSort framework is proposed. The CBAM attention mechanism is introduced into the backbone network of YOLOv7 algorithm to improve feature extraction ability. To improve feature fusion ability at different scales, BiFPN weighted feature pyramid is used to replace PANet, and a small target detection layer is added to improve the detection accuracy of small target UAVs. A "low slow small" human-machine data set is constructed with four types of backgrounds: sky, trees, buildings, and dark conditions. The experimental test is carried out. The results show that the detection part mAP_@0.5 of the improved algorithm is improved by 8.6%, and the detection accuracy of small-size and weak-feature targets is improved by about 21%. In the final tracking result, the MOTA index was increased by 24%, and the correct output target box accounted for about 70% of the true target box.

Camouflaged targets detection in low-light environments is one of the challenges in the field of deception detection. Especially with the continuous advancement of camouflaged technology, targets are highly integrated with their environmental background. Poor lighting conditions can often lead to performance degradation in conventional single-modal detection algorithms. To address this issue, this paper proposes a feature-level fusion network guided by the object detection task. First, this paper designs a residual dense connection to extract and stack information from multiple dimensions, enhancing the prominence of the target within the original information to obtain fused features of camouflaged targets. Then, the fused features are fed into the YOLOv7 network for camouflaged target detection. By optimizing the loss function and integrating spatial-channel attention mechanisms, the detection performance of camouflaged targets under low-light conditions is effectively improved. Additionally, this paper constructs an optical-infrared camouflaged target dataset for low-light environments to validate the proposed method with empirical data. The dataset shows an mAP@0.5 of 87.38% and a precision (P) of 85.45%, indicating that the proposed algorithm has a detection advantage for camouflaged targets under low-light conditions.

In order to meet the development requirements of unmanned aerial vehicle (UAV) boosters, a detachable launch booster for fixed wing UAVs was designed, which is driven by high-pressure carbon dioxide gas. A mathematical model for gas launch was established based on the internal ballistic equation of the gas cannon. Through theoretical calculations and the use of dynamic simulation software ADAMS to simulate the designed 3D model, the variation laws of initial velocity, overload, displacement, etc. during the launch of unmanned aerial vehicles of different masses were obtained. The results show that the initial velocity, displacement, and overload variation curves obtained through simulation when the drone leaves the launch pad are highly consistent with the theoretical calculation results, verifying the correctness of the calculation and simulation. The research results indicate that the fixed wing unmanned aerial vehicle (UAV) booster launcher can successfully launch UAVs weighing 14 kg or less and achieve initial takeoff speed without exceeding maximum overload. The simulation results have an error of no more than 3% compared to theoretical calculations. The research results can further provide theoretical guidance for gas emission.

A set of unmanned aerial vehicle (UAV) swarm combat effectiveness evaluation methods based on combat process simulation is constructed to address the issue of UAV swarm combat effectiveness evaluation in uncertain situations. Firstly, analyze the operational process of unmanned aerial vehicle (UAV) reconnaissance and strike missions from the perspective of closed-loop combat environment. Secondly, design a combat method calculation module based on the various steps of the closed-loop combat environment, and combine the designed parameter setting module, battlefield situation generation and recording module, global progress controller, and combat data recording module to form a complete simulation system for unmanned aerial vehicle group observation and combat process. Then, based on the available simulation data of the combat process, construct core effectiveness indicators and auxiliary effectiveness indicators, and provide a calculation and coupling method for each indicator. Finally, a scenario is set up to simulate the combat process of unmanned aerial vehicle swarms using different deployment schemes, evaluate and analyze the combat effectiveness, verify the feasibility of the proposed method, and achieve effective evaluation of the combat effectiveness of unmanned aerial vehicle swarms under uncertain situations.

Focusing on the urgent need of joint operational plan simulation experimentation, aiming at the lack of intelligent decision-making ability of command entity in the current joint operations simulation systems, and the difficulty of popularizing the simulation experimentation pattern of man-not-in-loop, this paper researches a joint campaign level simulation intelligence which named virtual commander in the simulation loop to simulate the thinking and behavior of human commander’s operation decision-making. This paper puts forward the definition and orientation of virtual commander’s operational decision-making, constructs a framework of virtual commander’s operational decision-making model, and designs the virtual commander’s operational decision-making thinking model, behavior model and simulation model based on the theory of natural decision-making, which provides a feasible solution to the bottleneck problem of simulation experimentation pattern of Man-Not-In-Loop.

In the face of the continuous growth of demand for observation missions, it is increasingly critical to execute reasonable and efficient mission planning for more and more satellite resources. In order to better grasp the current research hotspots and trends of satellite observation mission planning, this paper combines the bibliometric analysis and content analysis methods. First of all, by using science knowledge mapping tool CiteSpace, the visualization analysis of keywords in this field is carried out, drawing a conclusion that the main hotspots are distributed collaboration mission planning methods and planning solving algorithms. Then, the researches on these two issues are respectively discussed. On this basis, the development directions of this field that can be focused on in the future are forecasted at last. This paper provides reference for the research and application of satellite observation mission planning.

The emergence of unmanned surface vehicle(USV) attacks in the battlefields of Russia and Ukraine poses a significant threat to surface combat forces and critical coastal infrastructure, making it challenging to intercept by conventional countermeasures. In order to address the combat requirements in future warfare scenarios, the U.S.Department of Defense Architecture Framework(DoDAF) is adopted to model anti-USV swarm combat system architecture.A battlefield simulation software is used to test and evaluate the combat system, which verifies the effectiveness of the system. The research result can provide some reference for future studies on the application of anti-USV swarm combat.

Aiming at the problem of effectiveness analysis of naval gun system based on combat effect, the effectiveness analysis model is established by ADC method and analytic hierarchy process. According to the combat task, the evaluation models of availability vector, reliability matrix and capability vector are given respectively. In the evaluation model of capability vector, it is proposed that the combat task is related to the impact effect, mission time and ammunition consumption, and the specific calculation model is given in combination with the analytic hierarchy process, which provides a strong support for commanders to make decisions. Through calculation and comparison, the best execution scheme is selected, and the weight of parameters can be re-calculated according to the actual situation, so as to provide a more reasonable and accurate reference.

With the rapid development of artificial intelligence technology, intelligent weapon equipment is playing an increasingly prominent role in future urban warfare. This paper aims to propose a scientific method to determine the development strategy for intelligent weapon equipment for urban combat. Firstly, the characteristics of urban warfare and the development status of intelligent weapon equipment are analyzed. The fuzzy analytic hierarchy process (FAHP) is innovatively introduced into the SWOT analysis framework. This model utilizes FAHP to process the subjective SWOT analysis matrix, quantitatively calculating the weights of influencing factors, and improves the deficiency of traditional SWOT analysis relying too much on expert experience. Meanwhile, an improved calculation method for the centroid coordinates of the strategic quadrilateral is proposed, presenting a more reasonable coordinate calculation formula that can more accurately determine the centroid position. Finally, taking the intelligent weapon equipment for urban warfare as an example, the opportunity-seeking and proactive strategy is determined through calculation, and suggestions for the corresponding development strategies are put forward.

The research of equipment failure distribution is the basis of reliability engineering. In view of the complex structure and failure mechanism of wireless communication equipment, numerous failure distribution types, difficulty in selecting and evaluating reliability models and other problems, this paper starts from the research on equipment failure distribution at the bottom level, takes typical wireless communication system vehicle self-organizing network equipment as the research object, and compares and analyzes the correlation coefficients and error values of equipment life exponential distribution, lognormal distribution and two-parameter Weibull distribution. By introducing the information criterion and the special goodness of fit test to analyze the fitting effect of the reliability model, the reliability analysis method of wireless communication equipment based on model selection is proposed, and the test and analysis are combined with actual cases to improve the accuracy and effectiveness of model selection.

Computer simulation is a crucial approach for advancing research in intelligent aerial combat. However, existing aerial combat simulators are often non-open-source, challenging to develop, poorly visualized, and difficult to integrate with advanced AI technologies. This paper introduces a 3D aerial combat simulation system based on NetLogo 3D and HubNet. The system constructs static models of terrain, aircraft, and missiles in NetLogo 3D, and encapsulates functions to implement dynamic behaviors such as aircraft maneuvers and missile attacks. The system not only supports expert algorithms but also integrates DDQN reinforcement learning algorithm via Python extensions, enabling intelligent agents to make maneuver and attack decisions. A C-S architecture is employed via HubNet to support various simulation scenarios, including human-human, human-machine, and machine-machine engagements. Experimental results validate the system's effectiveness and stability, highlighting its real-time visualization capabilities and rapid integration of AI algorithms.

The size of the radar network coverage area is one of the important indicators to measure the performance of the air defense system, and it is of great significance to optimize the layout design of radar network and to maximize the comprehensive uniform chamber benefit of radar network. Since the probability of detecting a target by the radar network is the result of the joint action of various radars, the graph of its coverage area is very complex and difficult to obtain an analytical solution by conventional methods. By analyzing the relationship between the joint probability of multiple radars detecting a target and the radar parameters and target distance, the constraints are determined, and the basic idea of Monte Carlo method is used to transform the area calculation problem into a problem of calculating the number of points satisfying the constraints in a plane. The process of the specific algorithm is given. Compared with similar algorithms, the structure of this algorithm is clear and simple, and it is easy to extend. Through simulation experiments, the feasibility of the algorithm is verified.

In order to design satellite with low cost and high effectiveness, an optimization method of life cycle cost-effectiveness for satellites is proposed in this paper. Since most satellites are unrepairable and their performances experience degradation in orbit, a life cycle effectiveness model based on ADC model is constructed. Then, a multidimensional parameter cost model is proposed to estimate the satellite cost. Finally, an optimization model and the solution method are developed by synthesizing the life cycle effectiveness model and the multidimensional parameter cost model based on CAIV. A remote sensing satellite is adopted to demonstrate the proposed method, the result suggests that this method could optimize the performance, reliability and weight indicators of satellite system, which might be applied for project design demonstration, task management, and life cycle cost control of satellites.

Aiming at the typical air threats faced in landing operation,this paper focuses on air situation assessment and decision model of electronics and firepower collaborative interception in process of high mobility ultra short range air defense operations. According to the relevant interception model, combat vehicle such as electronics and firepower are managed to solve the collaborative problem between firepower strike and electronic warfare,radar detection and electronic warfare, command communication and electronic warfare. Related models can achieve the combination of multiple combat vehicle.

The optimization of operational plans is the key phase of operational planning, and the decision quality is directly related to the success or failure of the war. Aiming at the optimization question of operational plans, this paper proposes optimization of operational plans based on BWM-QFD method. Firstly, this method builds the house of quality on optimization of operational plans, and establishes the connection of plan satisfaction requirement and capability index. Secondly, this method uses BWM to calculate the weight of plan satisfaction requirement, builds the optimization framework of operational plans, and proposes the evaluation algorithm of plan satisfaction requirement. At last, through the case of optimization of operational plans, the effectiveness and the implementability of this method is numerically verified.

Special information system is a complex information system with specific architecture, clear functional positioning, and diverse components and relationships among elements, and the key point refers to the node or component element that plays a key role in the system, and the damage of "acupuncture point" will have a huge impact on the function and utility of the whole system. It is of great scientific value and practical application significance to study how to excavate the key points of the system. In this paper, the complex network theory is used to analyze the network characteristics of special information system. On this basis, from the two dimensions of "static" and "dynamic", five indexes are proposed respectively, namely, transcendence, clustering coefficient, eigenvector centrality, network elasticity and network destruction resistance, which are used to evaluate the importance of each node. Next, YAANP decision software is used to determine the weight of each index, and then a key "acupoint" mining method for special information system is proposed. Finally, taking the special information system under the background of urban unmanned attack operation as an example, the scientific and reasonable of the proposed method is verified.

This paper mainly studies the deployment scheme of the single base sonar and bistatic sonobuoys with the least number of sonars when blocking the potential area of the target submarine. Under ideal sea conditions, the minimum amount of sonar is required when the intersection line between the detection area and the target area is the diameter of the detection area. Therefore, a bistatic optimal array scheme is proposed in this paper. In the same equivalent radius, the difference between the number of sonars required by this scheme and the optimal array of single-base sonar is only -1 to 1. Finally, this paper verifies the effectiveness of the deployment scheme through experimental simulation, and calculates the number of sonar required for each scheme, which has guiding value for the anti-submarine area blockade.

The traditional perception of main elements of "C⁴KISR/C⁴ISR" cannot represent relationships between computers, communications and the other 5 military elements. A recognition of C⁴KISR system is proposed from the perspectives of architecture and dynamic information flow, basic and multi-C⁴KISR architectures are constructed, the dynamic process of information flow is analyzed, and computer and communication efficiency and their impact on the system are focused on. "C⁴KISR" cannot be regarded as just a command & control system from the perspective of the entire architecture; it is necessary to enhance the synergy of all elements to enhance the efficiency of the system, otherwise weaknesses or blockades of the system may be formed. Above research has important reference for rational construction of "C⁴KISR".

The boat based on anti-unmanned system launch and control designed in this article integrates 70 mm unit, 40 mm unit and anti-armor unit. With the help of detection equipment, it can effectively deter and strike small and medium-sized UAVs, unmanned helicopters, suicide USVs, etc. in the airspace below 5 000 meters. The control unit adopts ARM+FPGA master-slave control structure, which fully plays the advantages of two different chips, and innovatively designs the hardware execution sequence of the launch process in the hardware circuit. While executing in strict accordance with the launch process sequence, it can ensure that no misfiring is caused by damage to any component, further ensuring the safety and reliability of the system.

A multi-user shortwave data link algorithm based on multi-domain orthogonal frequency selection is proposed to address the difficulties in frequency selection, low timeliness, susceptibility to interception and interference during the fixed frequency construction of shortwave data link, as well as the problem of mutual interference during the multi-user simultaneously building the link. Firstly, the algorithm segments the frequency bandwidth using multi-domain orthogonal planning based on the number of users and frequency interval, and then obtains the set of available frequency points for each user based on the disabled frequency and frequency demand. Finally, the system frequency-selecting chain building function is used to establish the links for multiple users simultaneously. The algorithm effectively solves the problems of frequency selection difficulty, frequency conflict, link instability, and frequency exposure in multi user simultaneous chain building, effectively improving system availability and anti-interference ability.

In response to the issue that existing trajectory classification methods fail to fully consider the time series features and spatial structure features of trajectories, leading to a decline in classification accuracy, this paper proposes a trajectory hierarchical classification method based on deep learning networks. First, ship trajectories are transformed into image layers, and a trajectory image layer classification model based on the Swin-Transformer network is constructed. Next, for the trajectory sequence layer, a multi-dimensional information-based trajectory compression algorithm is utilized to optimize the input of trajectory sequences, and a trajectory sequence layer classification model based on the Gained-Transformer-Network deep learning network is developed. At last, a confidence-based fusion layer trajectory classification model is established to improve the accuracy of layered trajectory classification. Experimental validation shows an average classification accuracy of 90%, with the performance of the ensemble classifier improving by an average of 11% compared to other single classifiers, and an average F1 score of 0.82. The results indicate that the newly proposed method and the new ensemble classifier demonstrate good classification effectiveness for ship trajectories.

In order to push forward “The Third Offset Strategy” and enhance the ability of globally integrated operations, the U.S military proposes all-domain command and control (JADC2). JADC2 owns 14 abilities in 4 aspects such as joint all-domain situation cognition, joint all-domain AI-aided decision, joint all-domain command and action control as well as joint all-domain support. In order to systematize these core abilities in JADC2, the U.S military develops supporting techniques such as advanced system structure, operation cloud, military internet of things, military 5G communication, big data and supporting systems from the U.S military multiservice. In the future, the U.S military will promote the ability construction of JADC2 using the ways of four-side developing, combat verifying and AI energizing.

Aiming at the problem of how to select high risk key defense area under the condition of limited urban defense force, to maximize the utility of limited force and minimize the urban risk, this paper proposes a method of selecting key defense area based on regional comprehensive risk assessment. Firstly, a reasonable risk assessment index system is constructed from the perspective of the intruder and the defender. Secondly, based on the idea of equivalent energy, the weight product normalization method is proposed to determine the combined weight of the target risk index, and then calculate the target risk value. Then, according to the centroid theory, the weighted centroid calculation model of target distribution is constructed, and the dispersion matrix is established to calculate the dispersion of regional target distribution. Finally, the regional comprehensive risk assessment function is constructed according to the area of risk radar map, and the comprehensive risk of each region is quantitatively assessed according to the target situation of districts I to VI of a city. The results show that compared with the target risk coupling method, the proposed method can realize the regional comprehensive risk assessment more accurately, and provide decision-making basis for the selection of key urban defense areas and the efficient deployment of limited forces.

During the operation of rocket mine laying system, due to the influence of wind speed and direction, the landing position of the mine is prone to deviate from the predetermined position after opening the cabin, which leads to the decline of mine laying operation efficiency. In this paper, a model for evaluating the operational efficiency of rocket mine laying bomb is established, the relationship between wind speed and wind direction and the landing position of rocket mine laying bomb is simulated and analyzed. The operational efficiency of rocket mine laying bomb is verified. The simulation results show that the rocket mine laying bomb is affected by crosswind level Ⅳ, and the landing position deviates greatly from the predetermined position. When the longitudinal wind speed reaches level VI, the land position of the rocket mine bomb deviates greatly from the predetermined position. The research results demonstrate the quantitative relationship between the operational efficiency of rocket mine laying bombs and wind speed and direction, which provides methodological support for the rational use of rocket mine laying system in actual combat environment.

With the global and multidimensional transformation of war forms and the rapid development of intelligent technology, cross domain collaborative intelligent systems have gradually become an important development direction to meet diverse mission requirements and improve mission efficiency. In response to the issue of ineffective implementation testing of cross domain collaborative intelligent systems, a study is conducted on the system composition and architecture design, system integration and information flow design and simulation testing process of the LVC simulation system for cross domain collaborative intelligent systems; The design of a multi granularity LVC simulation system that includes practical nodes, semi physical nodes, digital nodes and intelligent algorithm models is completed; Finally, simulation experiments are conducted based on specific scenarios, and the results show that the LVC system can effectively meet the multi granularity simulation test requirements of cross domain collaborative intelligent systems.

Nuclear power stations are important pillars of the national nuclear system, whose defense security is an important guarantee for ensuring the safety and sustainable development of nuclear energy, providing support for national energy security. Based on the possible means of attack by war risk or terrorists, the realistic risks faced by nuclear power plants, such as violent terrorist activities, ultra-low altitude drone attacks, and cyber electronic attacks, are analyzed and the characteristics of various risks and threats are described. Three categories of detection and navigation, combat performance and destruction effect are distinguished, and 11 typical factors affecting risk generation are proposed. The logical relationship and mechanism of enemy "detection-fight-consequence" is analyzed, and the index evaluation system of influencing factors of nuclear power station attacked risk is constructed, which provides theoretical and methodological support for scientific measurement of the attack risk faced by nuclear power plants, targeted strengthening of nuclear power plant defense measures and optimization of emergency force application.

This paper focuses on the deployment method and implementation of command and control software, and focuses on the rapid deployment of applications. Based on the practical testing of container technology's technical conditions and advantages, this paper analyzes the potential application prospects of container technology in charge software. It proposes a design concept for rapidly deploying charge software using containers, while also analyzing the shortcomings and defects of traditional deployment methods. Furthermore, it designs a logical architecture and summarizes key technologies that need to be addressed for rapid deployment and solving difficult problems. This provides valuable reference for further improving and upgrading the software.

As the only high-speed navigation equipment in the ocean, the target recognition performance of the underwater high-speed vehicle determines the final completion effect of the mission. Due to the complexity of marine environment and the constantly upgrading of new countermeasure equipment, underwater high-speed vehicles are currently faced with the problem of insufficient recognition ability in complex marine environment, and it is urgent to find a new way of feature extraction and target recognition. Based on the good feature mining ability of deep convolutional networks and the characteristics of echo signals, a deep learning underwater target recognition model is proposed in this paper, and the model verification experiment is carried out by using test site data. At the same time, to solve the problem of insufficient training data, a generative adversarial networks is established to expand the data set. The experimental results show that the deep learning model proposed in this paper can effectively identify underwater targets, and the model recognition accuracy is improved by generating adversarial network data set expansion, which provides a new idea for the intelligent development of underwater high-speed vehicles.

A large amount of textual information will be generated in the exercise training process, and enormous cognitive pressure will be exerted on training evaluators due to complexity and diversity of such information.How to fully extract unstructured data from exercise training documents and provide efficient services for analysis and evaluation personnel is a challenging issue in data processing. In this paper, we propose a deep learning-based event extraction technique for exercise training documents, which addresses the characteristics of abundant professional terminology, coexistence of Chinese and English, and dense key information in short sentences. By leveraging the powerful text feature extraction of ALBERT and the structured prediction of CRF sequence labeling, we construct an event extraction model for exercise training documents. Experimental results on the training data set demonstrate that this model performs well in text extraction and has practical applications for extracting information from exercise training documents.

The formation movement of Autonomous Underwater Vehicle (AUV) clusters has significant application value in underwater missions. To achieve efficient collaboration and formation movement of AUV clusters in underwater mission scenarios, this paper proposes a method for AUV cluster formation movement based on directed communication constraints. This method utilizes the directed communication limitations between vehicles in real underwater environments through simulation to mimic communication conditions and employs a self-developed method to control the relative positions between follower vehicles and leader vehicles within the cluster. This approach thereby enables perception and communication between AUVs, adaptively adjusts formations, coordinates actions, and effectively responds to task requirements and environmental changes. This paper introduces the principles, design framework, and implementation process of the proposed method, and verifies its effectiveness through simulation and experimental results.

The formation and obstacle avoidance control of multi-Agent systems, as an important research direction in the field of intelligent transportation, is widely applied in military and civilian environments due to its high practicality. The traditional periodic sampling update mechanism has limited effectiveness in handling non-ideal conditions and its high resource demand leads to significant system resource waste. To address this issue, this paper, using an autonomous surface vehicle model as a background, proposes a multi-Agent system formation consistency algorithm based on event-triggered control and the leader-follower method, incorporating a directed graph structure into the algorithm. Utilizing Lyapunov stability theory, this paper provides a rigorous mathematical proof of the stability of the proposed algorithm, demonstrating system stability while avoiding Zeno behavior. Furthermore, under the premise of maintaining formation consistency, this paper implements obstacle avoidance and collision avoidance functions using an improved artificial potential field method. Experimental results verify the significant advantages of the improved artificial potential field method in obstacle avoidance performance.

In order to solve the problem of boosting cognitive MIMO radar for multiple moving target detection in cluttered backgrounds, this paper constructs a multi-target optimization model based on the dual mutual information criterion, takes into account the problem of linear variation of the motion target impulse response (TIR), estimates the TIR at the next moment by using Kalman filtering algorithm. Then the optimal frequency-domain waveforms by using the water-filling algorithm is adopted, and the time-domain waveforms of the cognitive MIMO radar by using the genetic algorithm is synthesized. With simulation verification, the algorithm can meet the needs of MIMO radar to observe multiple targets and realize the effective estimation of the impulse response of moving targets, which can improve the performance of multiple moving targets detection compared with the traditional fixed signal.

To meet the precise maintenance support requirements of rocket guns, a method for selecting fault repair strategies based on FMEA and fuzzy closeness is proposed. This approach enhances traditional FMEA by incorporating fuzzy theory to evaluate fault risk indicators, and establishes standard fault models corresponding to different repair strategies. The most suitable repair method is then determined by comparing the comprehensive closeness between the rocket gun faults and the standard fault models. The method's feasibility is demonstrated through a case study involving a rocket gun's fire control computer. The analysis reveals that this approach simplifies the evaluation process, delivers more reasonable and accurate results, and effectively meets the practical demands of rocket gun maintenance support.

The traditional model for calculating the destruction probability of anti-aircraft gun burst firing uses the method of calculating the average number of hits required for destruction, which obscures the differences in elements such as firing elements, projectile-target range, and hit conditions. With the demand for precise destruction, this model is no longer suitable. In this paper, a precise destruction probability calculation model is studied based on the collision and destruction mechanism of projectile-target engagement. Starting from the definition of a hit, a hit model based on the projection method of the projectile is established, and then the destruction probability upon hit is calculated based on the collision and destruction mechanism. With the aid of external ballistics, the burst firing process is simulated. Finally, the Monte Carlo method is used to calculate the average value of the simulation statistics. The simulation results show that the calculation method in this paper can more accurately reflect the actual destruction situation of the anti-aircraft gun weapon system against the target.

Aiming at the problem that the data of the whole system flight test is limited, the environmental factors and reduced algorithm have been introduced into the evaluations of the missile flight reliability in this paper, and the equivalent conversion method of multi-source reliability test data has been investigated. Primarily, the component test data has been converted into the whole missile data by applying equivalent conversion method; then, the ground test data has been converted into flight test data by applying Weibull distribution environmental factors; finally, the missile flight reliability has been evaluated by applying the converted data. The numerical examples have also been given to verify the validity and feasibility of the method, which effectively expand the number of evaluation samples, and improve the overall quality and efficiency of the test.