The detection and recognition of small targets in complex naval combat scenarios is a critical component of achieving intelligent perception in modern naval operations. This paper provides a systematic review of the development of small-target detection technologies in such environments. This paper examines key technological advances in detection and recognition, including performance under adverse weather conditions, ship-type identification, and multi-sensor and multimodal fusion approaches. Based on battlefield requirements, this paper also explores potential future directions. Under the in-depth empowerment of artificial intelligence technology, the small target detection and recognition technology in complex naval battle scenarios is accelerating its evolution towards automation and expansion towards cluster collaboration. Meanwhile, its level of intelligence has been significantly improved, laying a solid technical foundation for the realization of all-weather and all-dimensional maritime information perception capabilities.

In view of the decomposition and planning of missions at sea, this paper proposes a reverse mission decomposition and parameter modeling method based on the killing chain. Based on the killing chain combat theory, the four core task modules of search, tracking, strike and communication have been constructed, the functional boundaries and internal logic of each task type have been clarified, a unified task unit model has been built, and the operational sub-tasks have been formally modeled and algorithm flow designed. Adopt the reverse task decomposition mechanism, aim at the requirements of strike effect, and push back the parameters and resource conditions required for the front task step by step to ensure the consistency of the task chain parameters and the logical closed loop of the combat process. By constructing task decomposition assumptions and task instance decomposition, the advantages of this method in terms of task coverage, resource scheduling efficiency and execution accuracy are verified, and effective theoretical and technical support is provided for the coordinated operation.

The application of advanced technological means and new types of weaponry make it increasingly difficult to rely solely on human thinking and physical strength to handle the intelligent operations characterized by “high-intensity, fast-paced, and highly complexity”. Unmanned equipment, with its prominent features such as “withstanding harsh conditions and strong battlefield survivability”, can make up for the deficiencies of combat units in the operational process. Thus, manned/unmanned cooperation has become an effective means to counter powerful adversaries. For this reason, this study first outlines the connotations and characteristics of manned/unmanned collaboration, classifies the manned/unmanned collaboration levels, and analyzes the winning mechanisms of manned/unmanned collaboration combat. Then, it systematically reviews the key technologies of the manned/unmanned collaboration system based on the two-level framework of “basic supporting technologies-collaborative core technologies”. Finally, it explores ways to enhance the capabilities of manned/unmanned systems and puts forward systematic suggestions for countering and neutralizing adversarial measures against such collaboration systems, forming a research chain of “capability requirements-key technologies-development suggestions” and hoping to provide valuable references for future theoretical research on manned/unmanned collaborative operations.

To address challenges in intelligent mission planning, including subtask decomposition, battlefield event ambiguity, and ethical trade-offs, a system architecture based on human-machine cognitive collaboration is proposed. By deconstructing cognitive processes into five dimensions (situational awareness, assessment, plan generation, evaluation, and action control), a capability complementarity model is established, optimized through dynamic weight allocation. The three-layer architecture comprises: a data support layer integrating operational rules, historical cases, and psychological monitoring for multi-source fusion; an intelligent processing layer utilizing knowledge graphs and hybrid reasoning engines for situational deduction, ethical assessment, and innovative planning; and a human-machine collaboration layer enabling bidirectional cognition via neural enhancement, ethical constraints, and dynamic task negotiation. Some reference for improving the quality and efficiency of human-machine collaboration mission planning system can be concluded from the study.

Cooperative game theory provides a systematic mathematical modeling tool for cross domain collaborative combat by studying the formation of alliances, distribution of benefits, and collaborative optimization mechanisms. This paper systematically explains cooperative game theory and proposes a military alliance game system framework, analyzes its application mechanism in scenarios such as joint firepower strikes, integrated air and missile defense, and network electromagnetic countermeasures, and reveals its optimization effect on multi domain collaborative efficiency. Further explore the technological challenges of incomplete information and security constraints in dynamic battlefield environments, and propose future research directions for the integration of dynamic game theory, robust optimization, and artificial intelligence. This theory is a key capability support for building an intelligent combat system and enhancing the combat effectiveness of cross domain systems, and has important military application value.

With the advancement of multimodal large model (MLM) technology in capabilities such as information comprehension, logical reasoning, and content generation, its application in the military domain has reached a stage of maturity. This paper proposes a deception countermeasure architecture for command information systems based on MLM technology. First, the necessity of architectural research is clarified by examining the relationship among command information systems, military deception, and MLM technology. Subsequently, the deception countermeasure architecture and specific strategies are detailed at both the holistic and subsystem levels. Analysis demonstrates that the proposed architecture offers significant advantages in real-time performance, scalability, and flexibility. It can effectively disrupt enemy decision-making processes and prevent enemy deception operations, thereby providing engineering insights and theoretical support for the intelligent transformation of command information systems.

Systematized equipment, as a crucial prerequisite for informatized warfare, represents a major feature of modern weaponry. To address the issues in systematized equipment testing, such as the lack of standardized norms, insufficient environmental simulation capabilities, and deficiencies in systematic evaluation, this paper first elaborates on the concept of systematized equipment. It then analyzes the current state of systematized equipment testing and evaluation both domestically and internationally, focusing on testing models, methodologies, and capabilities. A comparative study is conducted to identify the existing problems and gaps in China’s systematized equipment testing and evaluation. Finally, in light of domestic systematized equipment testing requirements, particularly those related to combat readiness and informatization assessment, this paper proposes solutions and key measures to tackle the challenges in China’s systematized equipment testing and evaluation. The findings hold significant reference value and practical importance for advancing research in systematized equipment testing and evaluation technologies.

In response to the demands of large-scale cross-regional networked tactical confrontation flight simulation training, an intelligent simulation platform architecture based on cloud-edge-end collaboration has been designed. The architecture supported by big data and centered around intelligent large models, intelligent agents and real-time simulation models, enables multi-modal operation integrating peacetime and training running modes through cross-regional cloud-edge-end resource integration, dynamic reuse, collaborative simulation and virtual-real fusion. Based on an analysis of the functional requirements of this intelligent simulation platform, the study focuses on designing its hierarchical architecture, network structure, and synchronized simulation strategies. It investigates critical aspects including cloud-edge-end simulation task allocation, operational modes, and application scenarios. Three key technical challenges are explored including integration of cloud-based XR and AI technologies, cloud-based convergence of big data, large models and real-time simulation, and real-time interactive cloud-edge-end collaborative simulation. This work provides a reference model for constructing a new-generation intelligent simulation platform that supports all-scenario, full-system, multi-element training, thereby advancing intelligent transformation in flight simulation training domains.

Modern computer wargames have been widely valued and applied. However, the ideal assumption in wargame systems that the observation among friendly forces is completely shared is far from the reality, which limits the simulation of many real-world situations. Therefore, in order to solve the above problems, this paper proposed a wargaming system framework with incomplete observation, where seats are the participants, and the wargaming simulation process is sorted out, including the model library, simulation engine, observations distribution, and front-end deduction. Then, referring to and comparing with mature wargame systems, we elaborately designs the elements of the wargame system related to the incomplete observation, such as seat model, communicator operator, intelligence model, communication action and adjudication, as well as the generation process of the incomplete observation. Finally, a red-blue confrontation scenario with three seats versus four seats was designed to verify the feasibility and effectiveness of the wargaming system described in this paper.

Core technologies for online mission planning of hypersonic glide vehicles—rapid maneuverability assessment and autonomous trajectory planning—are ushering in new development opportunities with breakthroughs in artificial intelligence and onboard computing capabilities. By systematically reviewing research progress and trends of four representative methodologies in autonomous trajectory planning and footprint determination (including drag acceleration profile methods, quasi-equilibrium glide condition solutions, rapid optimization techniques, and deep reinforcement learning approaches), this study conducts in-depth comparative analysis. Finally, prospects are provided for key technical directions and challenges in rapid maneuverability evaluation and autonomous trajectory planning, focusing on high-precision intelligent dynamic modeling, collaborative autonomous trajectory generation, and physics-informed neural networks, aligned with the evolutionary trends of glide vehicle technologies and AI advancements.

To address the limitations of traditional cybersecurity technologies in responding to complex cyberattacks and the challenges faced by large language model (LLM) in cybersecurity applications (e.g., hallucinations, outdated knowledge bases, and insufficient interpretability), this paper proposes a construction method for a cybersecurity large model based on the Retrieval-Augmented Generation (RAG) architecture. Through three stages—functional analysis, architectural design, and technical implementation—this approach completes the "4-layer, 1-module" construction and practical deployment of the RAG system, bridging the gaps in applying generic LLM to cybersecurity domains. Furthermore, this study innovatively introduces mechanisms including parameter adaptive optimization, metadata-filtered retrieval,dynamic permission management and multi-round evaluation feedback to ensure the model’s usability, trustworthiness and security.

As one of the core elements of the naval battlefield, situational awareness plays a vital role in supporting commanders to grasp battlefield information in an all-round way and ensuring the efficient execution of command decisions. Starting from the situation of future wars, this article studies and analyzes the key intelligent projects in the field of situational awareness of the US Navy under the guidance of the new combat concepts of the US Navy, the development strategy of artificial intelligence of the US military, and combat concepts. It also summarizes its key technologies and suggestions for the application of artificial intelligence technology in the field of situational awareness by the navy.

Focusing on the problem of kill chain dynamic reconfiguration, this paper first analyzes the phenomenon where the failure of combat units due to enemy attacks or system malfunctions leads to degradation or interruption of kill chain effectiveness. It proposes operational robustness evaluation metrics that include strike effectiveness and survivability. Next, a bi-level optimization model for local dynamic reconfiguration of the kill chain is established, taking into account the location and impact of failed nodes, with the optimization objectives of improving strike effectiveness and survivability. Finally, comparative experiments are conducted to compare the proposed operational robustness evaluation metrics with traditional ones. The results show that the proposed metrics exhibit faster convergence speed and can effectively accelerate the dynamic reconfiguration process of the kill chain.

In response to the issue of assessing the decision-making superiority of a System of Systems (SoS), this study focuses on capability packages and kill chains, proposing a system decision-making optionality concept framework and a decision-making optionality measuring model. Corresponding calculation models for the availability of capability packages and the effectiveness of kill chains within the model are also developed. The research findings of this paper are innovative in the measurement of SoS superiority, especially decision-making superiority, and can be used to assess the combat potential of mosaic SoS and combat target analysis.

As a cutting-edge technology, multimodal large models are gradually transforming the information-processing paradigm in the military intelligence domain. This paper delves into the applications of multimodal large models in the military intelligence field. It first traces the development trajectory of multimodal large models, elaborates on the technological improvement paths of multimodal data and network models. Subsequently, it analyzes the application scenarios of five typical multimodal large models in future combat and conducts an in-depth analysis in combination with actual military intelligence platforms. This research reveals that multimodal large models constitute a crucial algorithmic component in intelligent warfare. Given their immense application potential in the military field, multimodal large models offer novel ideas and methods for military intelligence applications, thereby contributing significantly to the enhancement of the level of military intelligence.

Aiming at the typical training demands of commander training for joint operations teaching in military academies, such as mass joint operations knowledge inquiry, battle case traceability and re-pushing, combat scene cognition and blue army confrontation, four novel commander training modes are put forward from the perspective of top-level design which are the theoretical teaching oriented commander training mode with question answering, battle case study oriented commander training mode with extension, scenario teaching oriented commander training mode with cognition and countermeasure training oriented commander training mode with confrontation. Then the essential technologies such as military knowledge intelligent answering based on large language model, parallel battlefield and situational cognition, construction and analysis of military event evolutionary graph and blue army behavior tree modeling integrating online learning are analyzed in detail which support the four novel training modes. This study can provide methodological support and implementation reference for military academies to carry out commander training with certain characteristics of digital-intelligence integration.

UAV swarms are widely used in tasks such as personnel search and rescue, as well as military reconnaissance. In order to improve the efficiency of unmanned cluster in carrying out large-scale reconnaissance tasks, a multi-objective optimization model is constructed to minimize the flight time and maximize the detection revenue for the task allocation problem of UAV cluster with different sensors. By constructing integer task encoding and a population initialization method based on Voronoi partitioning, the quality of the initial solution is improved, and the genetic method in NSGA-II algorithm is restricted to shorten the optimization time. This algorithm can provide a set of non-dominated solutions, allowing for the selection of the shortest flight time or maximum profit plan based on preference. To cope with large-scale damage, an initial population is generated based on local task flow rules to achieve rapid task optimization. Simulation results show that compared to the original algorithm, the improved algorithm has significant advantages in task allocation and damage reconstruction of large-scale unmanned clusters.

Modern warfare is characterized by a trend toward multi-domain integration and intelligent development, with cognitive operations becoming a key domain in the competition for “cognitive dominance.” However, the current application of intelligent auditory stimulation in cognitive domain warfare remains unsystematic and lacks precise intervention models. To address this issue, the present study develops a model of an intelligent auditory stimulation system based on neuroscience, cognitive psychology, artificial intelligence, and biosensing technology. The study adopts multimodal data collection and real-time analysis methods, dynamically adjusting auditory stimulation parameters by monitoring soldiers' physiological and cognitive states, with the aim of achieving emotional stabilization, enhanced attention, and stress relief. The results show that the system can optimize psychological regulation in combat settings and enhance soldiers' team coordination and psychological resilience, thereby improving operational sustainability. It provides feasible decision-making support for the state and enhances strategic initiative. Furthermore, its application not only expands the research path of cognitive warfare theory, but also offers a scientific basis for constructing a cognitive domain warfare model suited to China's national conditions.

In response to the rapidly changing trend of systematic development in the field of hypersonic in the United States. Though the method of public literature analysis, we systematically sorting out the development of hypersonic management, equipment, technology, research and production and experimental support systems in the United States. From a macro perspective, this study explores the development of the United States in the field of investment, technological focus and trend direction.These can provide useful references for research in the field of hypersonic.

To address the challenge of designing and applying command and control architectures for operationally responsive space launches, the typical three-tier and four-tier architectures are studied. Based on entropy theory, a system entropy model for evaluating these architectures is constructed by integrating weighted timeliness entropy and quality entropy. The findings demonstrate that the three-tier architecture is superior when the weight coefficient of timeliness entropy (α) is less than or equal to 0.6, whereas the four-tier architecture proves more effective when α is greater than or equal to 0.67. Within the critical interval of 0.6 <α< 0.67, a threshold value u for the number of operational units (m) is identified: the three-tier architecture is preferable if m < u, and the four-tier architecture becomes optimal if m ≥ u. Furthermore, the threshold u decreases as α increases. Corresponding unit employment strategies, namely coupled utilization and streamlined utilization of combat units, are proposed for different launch modes and initial states of the launch vehicle. The models and conclusions can provide a theoretical foundation for decision-making in operationally responsive space launch command and control and its practical application.

Shipborne small-caliber weapons play an irreplaceable role in maritime rights enforcement and patrol missions conducted by Coast Guard vessels. Given the wide variety of existing small-caliber weapons, some models must be phased out based on the functional requirements and developmental goals of Coast Guard operations, while others require further improvements in tactical and technical performance. Consequently, it is imperative to establish a scientific evaluation method for assessing the operational effectiveness of these weapons. This study proposes an equipment effectiveness evaluation model based on the AHP-entropy weight method and fuzzy comprehensive evaluation. The model constructs an index system covering reliability, maintainability, combat capability, environmental adaptability, human-machine interaction, and cost-effectiveness. By fully utilizing objective factors from secondary indicators and combining subjective and objective weights, the evaluation results better reflect practical conditions. Focusing on three types of weapons, the paper compares their comprehensive operational effectiveness through case analysis. The findings offer theoretical references for decision-making in the configuration and modernization of shipborne small-caliber weapons.

Addressing the challenge of quantifying combat capabilities in complex military scenarios, this study proposes a novel capability measurement method based on the potential outcomes framework. This approach first define the object to be evaluated as an intervention variable and selects observable metrics that directly reflect operational effectiveness as outcome variables. Next, a causal graph is constructed, encompassing the intervention, combat capability, and operational effectiveness, thereby explicitly establishing the causal pathways between abstract combat capabilities and measurable effectiveness indicators. Finally, through causal effect inference and statistical significance testing, we transform the problem of measuring combat capability into a function of effectiveness indicators. Case analysis demonstrates that the proposed method, by utilizing effectiveness indicators as a measurement tool, can achieve indirect quantification of combat capabilities, offering a valuable reference for solving the quantification problem of combat capabilities.

In the task of image super-resolution reconstruction, this paper proposes an image super-resolution method called MSA-SR, which is based on multi-scale features and attention mechanisms. This method effectively captures the low-frequency and high-frequency features of low-resolution images by separating and extracting multi-scale features in both the time and frequency domains. On this basis, high-frequency guided cross-attention is used to selectively enhance high-frequency features, while wavelet convolution is employed to protectively enhance low-frequency features, achieving clear and natural image super-resolution reconstruction effects. The model was validated on the Urban100 and Manga109 datasets, and its performance metrics of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) showed certain advantages over other deep learning super-resolution methods. From a quality perception perspective, this model has made significant improvements in texture recovery, color restoration, noise suppression, and naturalness of the image, achieving superior visual effects, which proves the effectiveness and superiority of the model.

Aiming at the problem that the synthetic aperture radar (SAR) image detection model is difficult to balance the detection accuracy and model lightweight, this study proposes a lightweight SAR image target intelligent detection method based on YOLOv11 s. This method first replaces the backbone network with an efficient FasterNet structure, which significantly reduces the number of model parameters; secondly, the independently developed EMIBC module is innovatively integrated into the C3K2 module, which effectively improves the recognition ability of the model for small targets and multi-scale targets. Thirdly, the dynamic upsampling (DySample) is used to replace the traditional upsampling method to optimize the processing efficiency of the feature fusion stage. Finally, the Inner-SIoU loss function is introduced to replace the original CIoU bounding box loss, which further improves the training effect and feature extraction ability of the model. The experimental results on the HRSID dataset show that the improved model reduces the computational complexity index GFLOPs by 2.79 %, and the detection accuracy index mAP is increased by 7.35 %, which better realizes the balance optimization of model lightweight and detection accuracy.

Breaking down beachhead obstacle is the key content of the engineering support task of landing operations, and it is of great significance to accurately calculate the obstacle breaking requirements before the obstacle breaking operation begins. In this paper, the damage mechanism of the blasting bomb is analyzed, and a numerical calculation model of the damage efficiency of the breaking bomb is established, and the damage efficiency of concrete target plate and rock target plate with different thicknesses is simulated and evaluated. The simulation results show that the single-shot blasting bomb has a good damage effect on the concrete target plate and rock slab with a thickness of 50 mm, 100 mm and 200 mm, and the obstacle breaking operation can be achieved by firing one or more blasting bomb for the concrete target plate and rock target with a thickness of 500 mm. The research results can provide a method support for the rational use of the blasting bomb and the improvement of combat application efficiency under actual combat conditions.

It is a complex system engineering to construct a visual combat experimental system by using the technologies of LLM and LVM, which can not only improve the intelligent analysis ability and visual function of the combat experimental system, but also reveal the emerging ability of the combat system, and improve the generalization ability and self-monitoring learning ability of the combat experimental system. Based on the analysis of the key technologies of the visual combat experiment system with large model technology, this paper constructs the overall framework of the combat experiment system, and uses large language model (LLM), large visual model (LVM) and multi-Agent system (MAS) to simulate the complex dynamic interaction process between combat entities and the environment, so as to display the combat experiment process and intelligently analyze the combat experiment results in various modes and visual forms.

To address the issues of insufficient global optimality and robustness in traditional mission planning for unmanned aerial vehicles (UAVs) in urban disaster relief scenarios, this paper proposes a multi-heterogeneous UAV static task allocation algorithm based on improved genetic operators. By enhancing genetic operators, designing a functionally coupled chromosome encoding strategy, introducing adaptive crossover and mutation mechanisms, and embedding a path planning module into the task allocation algorithm, the accuracy of the task allocation cost function calculation is improved. This effectively overcomes the drawbacks of traditional methods, such as inaccurate allocation results due to path cost estimation deviations, as well as poor global optimization and robustness.

In recognition of the intricate structure and multifaceted nature of military communication networks, two typical models of such networks are established, respectively, from the viewpoints of interdependent and multilayer networks. With a particular emphasis on interdependent networks, an analysis is conducted on the importance of nodes and the robustness of military communication networks through node importance analysis and cascading failure assessments. To validate the models, a modeling and simulation is performed based on the communication network organization plan outlined in a military wargame scenario. The results obtained encompass the ranking of node importance and the impact of various factors, such as attack methods, node capacity, and load distribution, on the cascading failure process within military communication networks. These findings offer valuable insights for the analysis of connectivity and robustness in military communication networks.

Aiming at the problems of insufficient generality and expansibility, as well as poor adaptability to actual combat application scenarios in existing damage assessment simulation systems, the paper sorts out the full-link technical system of damage assessment covering target vulnerability analysis, ammunition power field analysis, damage effect simulation and comprehensive effectiveness evaluation. A four-layer architecture simulation framework for damage effectiveness assessment is constructed, which breaks the coupling constraints among data modules, algorithm models and functional components. The framework enables standardized management of damage resources and flexible combination of business applications, can provide theoretical support and technical approaches for the development of damage effectiveness assessment simulation systems under actual combat conditions.

To address the limitations of conventional radar intelligence support model, particularly their monotonous product categories and inability to meet personalized user requirements, this paper proposes an innovative order-driven radar intelligence support model. Drawing inspiration from the order-response mechanisms of ride-hailing service, the proposed model establishes a bidirectional interaction architecture between intelligence systems and users (i.e., supply-side and demand-side entities). Through dynamic demand-side interactions and agile supply-side responses, the model achieves optimal matching between intelligence production and user requirements. The operational workflow is systematically analyzed, with focused investigation of core technologies including granular demand decomposition and precision-targeted intelligence generation. Theoretical analysis and simulation case studies demonstrate that compared with conventional approaches, the new model expands intelligence product diversity while significantly improving service quality and efficiency. This research provides both theoretical foundations and engineering guidelines for implementing diversified and precision-targeted radar intelligence support systems.

A phased cooperative mission planning method based on an improved parthenogenetic algorithm and enhanced RRT* algorithm is proposed for UAV swarm search task planning. In the task allocation phase, the multi-target assignment is modeled as a Capacitated Multiple Traveling Salesman Problem. The improved parthenogenetic algorithm incorporates Minkowski distance for path cost estimation and a dynamically adjusted mutation probability strategy to enhance global optimization capability. During trajectory planning, the RRT* algorithm is upgraded through a "semi-circular sampling + artificial potential field" approach, combined with tangent arc transition method to optimize trajectory smoothness and quality. Simulation results demonstrate that compared with benchmark algorithms, the proposed method achieves a 53.2% faster task allocation speed, reduces total trajectory length by 9.2%, and generates trajectories that satisfy UAV kinematic constraints. This provides theoretical support for efficient cooperative mission planning of UAV swarms in complex battlefield scenarios.

To address uncertainties and multi-factor influences in combat capability evaluation of Armed Police Special Operations Units, this study establishes an assessment index system based on operational elements. By applying a game-theoretic combined weighting method to determine indicator weights and integrating grey-fuzzy modeling, we achieve effective fusion of qualitative and quantitative analysis. The proposed approach significantly improves the accuracy and reliability of evaluation outcomes, providing a scientific foundation for enhancing combat effectiveness development.

From the perspective of the operational effect of air defense missiles in the Russia-Ukraine conflict, the medium and long range air defense missile weapons were not able to effectively support the role of air and space shield, but were suppressed by hypersonic weapons and anti radiation weapons. Through the research on the air attack threat environment faced by air defense weapons in the Russia-Ukraine conflict, this paper analyzes that the new generation of medium and long range air defense missile weapons and equipment should be improved from four aspects: enhancing the system’s anti strike capability, building the reconfigurable air defense combat capability, strengthening the concealed combat and mobile deployment capability, and the missile cluster interceptor missile design capability. Finally, this paper puts forward the technical approach to the construction of the new generation of medium and long range air defense missile weapons and equipment. Through the prospect of the system effectiveness of the new technical approach, it shows that the new technical approach can effectively support the acquisition of air supremacy under the new form.

With the development of Industry 4.0 and smart manufacturing, industrial information systems face increasingly complex security threats, especially in data security and risk identification. This paper investigates data security protection and risk identification techniques in industrial systems, analyses data flow characteristics and identifies the security risks of supply chain collaboration and device IoT data, focusing on APT, supply chain contamination and data integrity attacks. A multi-dimensional security protection system based on zero-trust access control, homomorphic encryption, distributed anomaly detection and hybrid risk assessment is proposed for these threats. Experimental results show that the scheme performs significantly in intrusion detection, data encryption and risk assessment, improves the security of industrial control systems, and provides support for future industrial information security protection.

Camouflage deception technology stands as one of the key countermeasures against enemy reconnaissance and strike systems. This paper focuses on the latest developments in camouflage deception technology for ground equipment both domestically and internationally. It systematically reviews the research progress of major technical approaches—including decoy technology, SAR deception jamming technology, and missile luring and deflection jamming technology—from two dimensions: operational mechanisms and typical system equipment. Furthermore, it conducts an in-depth analysis of the development trends and technical pathways for camouflage deception technology in ground equipment. This work provides valuable references for subsequent exploration in this field and holds great significance for advancing camouflage and protection technologies for ground equipment.

To meet the route planning requirements of anti-ship strike missions of the aircraft, a multi-objective route planning model that incorporates safety threat, attack angle and range cost is constructed by analyzing the basic task process of air-to-sea strike actions and the main influencing factors, using geometric analysis methods. On this basis, an improved genetic algorithm based on fitness value calibration and similarity verification is proposed, which leads to the optimal route planning algorithm for air-to-sea strike operations, combined with the constructed mathematical model. Simulation results show that the designed model and algorithm can plan optimal two-dimensional routes for anti-ship strikes based on different mission information efficiently, which has solved the problems of global search, premature convergence, and late-stage oscillation found in traditional algorithms, while featuring low computational load, high accuracy, and strong practicality.

To address the optimization problem of task allocation for drone swarms, an improved hybrid DPSO-GA optimization algorithm is proposed. This approach constructs a complex mapping relationship for drone swarm task allocation under temporal constraints. It employs adaptive cosine adjustment for inertia weights and learning rates, while incorporating crossover and mutation operations to enhance the algorithm's global search capability, convergence speed, and accuracy toward extremes. Simulation comparisons with DPSO and GA algorithms reveal that the proposed algorithm achieves an average fitness value reduction of 50.0% and 10.7% compared to DPSO and GA respectively, with variance reductions of 95.7% and 79.9% compared to DPSO and GA respectively. The confidence interval widths were only 20.7% and 44.8% of those for DPSO and GA, demonstrating the algorithm's significant superiority in convergence, stability, and reliability over the comparison algorithms. This makes it a valuable reference for solving multi-objective task allocation problems in UAV swarms.

To address the current lack of a pathway from deduction simulation to evaluation in the assessment of UAV swarm operational effectiveness, a GraphRAG-based evaluation method was proposed, which fully utilizes deduction simulation data to reflect the operational effectiveness of UAV swarms in a realistic and objective manner. First, a knowledge graph was constructed using data from UAV swarm deduction simulations, such as attack, detection, and operational effect data. Subsequently, a Graph Retrieval-Augmented Generation (GraphRAG) system based on evaluation indicators was developed using the knowledge graph, combined with a large language model to intelligently generate a reasonable evaluation indicator system. Finally, the information entropy method was employed to achieve the operational effectiveness evaluation of UAV swarms. By constructing a UAV swarm ground attack operational scenario, the effectiveness of the proposed method was verified, opening a new methodological pathway for system effectiveness evaluation.

In response to the problem of large computational complexity and false detections in target detection by drones. A target recognition method based on lightweight networks has been proposed. Based on the YOLOv5 object detection algorithm, the algorithm has been optimized using the FasterNet lightweight network architecture, which reduces the number of network parameters and improves the efficiency of the algorithm. In order to accurately capture and emphasize key information in the input sequence, and further enhance the performance of the algorithm, a parameter free attention mechanism SimAM is introduced. The results indicate that this method is an optimized application of object detection technology, which can better balance the relationship between detection speed and accuracy, and achieve better detection results in unmanned aerial vehicle aerial image detection tasks.

Aiming at the problem that traditional equipment systems are difficult to adapt to the dynamic confrontation scenarios of intelligent operations, this paper constructs a three-level conceptual model of learning capability, namely "Single Equipment-System-Architecture". From three dimensions including data-driven adaptive learning, knowledge-driven collaborative evolutionary learning, and strategy-driven emergent evolutionary learning, a comprehensive evaluation index system integrating information theory, cybernetics, and game theory is established. Taking the collaborative assault of multi-domain unmanned swarms as a typical scenario, comparative experiments are set up. The verification results show that the model and index system can effectively distinguish the effectiveness differences of learning capabilities at different levels, accurately locate the learning bottlenecks of equipment systems in adversarial environments, and provide systematic support for the learning capability evaluation, optimization iteration, and operational application of intelligent equipment systems.