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.

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.

Multi-UCAV coordinated air combat will be one of the main combat modes of UCAV in the future. For the problem of dual-UAV formation air combat maneuver decision-making in UCAV coordinated air combat, an improved evaluation function UCAV dual-UAV formation air combat maneuver decision-making method was proposed based on the effective integration of prior knowledge such as air-to-air missile attack area and unescapable area. Firstly, the air combat situation evaluation function was improved based on the attack area and unescapable area of air-to-air missiles. Secondly, the basic operation modes of aircraft were combined with typical air combat tactical actions to establish a UCAV air combat maneuver action library. Finally, a simulation case was constructed, and the simulation analysis was conducted on the maneuver trajectory, advantage function,and target attack selection. The simulation experimental results show that the formation air combat maneuver decision model based on the improved evaluation function can enable the UCAV dual-UAV formation to make the correct decision against the enemy’s strategy. This method provides a reference for further research in the field of multi-UCAV air combat decision-making.

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.

To address the inefficiency and inaccuracy in water surface target detection and path planning for unmanned surface vehicles (USVs), this paper proposes a water surface target detection method based on LiDAR and a safe-region RRT* path planning algorithm based on KD-Tree. First, environmental point cloud data collected by LiDAR is processed using a bilateral filtering algorithm to filter out scattered and disordered noise points, thereby reducing the amount of data for subsequent processing. Next, the improved RANSAC algorithm is applied to achieve water surface segmentation, distinguishing between water and non-water regions. Subsequently, a multi-threshold Euclidean clustering algorithm is used to complete water surface target detection and identify water surface obstacles. Based on this, a path planning algorithm is designed to generate a safe and feasible navigation path for the USV. Simulation and experimental results demonstrate that the proposed method exhibits high reliability and effectiveness in water surface target detection and path planning, providing essential technical support for the autonomous navigation of USVs in complex water environments.

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.

The enhancement effect of the classic Nonnegative Matrix Factorization (NMF) applied to underwater acoustic target signal is unsatisfactory for the feature overlap of underwater acoustic target signal and the variability of ocean underwater acoustic field. And so, an improved NMF underwater acoustic signal enhancement algorithm based on feature preservation is proposed by adaptively improving the classic NMF algorithm using the characteristics of underwater acoustic target signals. The β-divergence constraints and similarity detection is first applied to the NMF feature basis matrix to eliminate redundancy, optimized NMF features with size invariant characteristics, while avoiding the loss of basis vectors due to coefficient dispersion caused by similar basis vectors. And then, the real-time environmental noise received by the sonar is used to improve the adaptability of the NMF noise basis vector, achieving noise reduction and enhancement of the underwater acoustic target signal. The experimental results show that, compared to the classical NMF, the manifold constrained NMF that is used to the underwater acoustic signal enhancement, the proposed method achieved the better signal enhancement effect.

This paper addresses the issue of low accuracy in electromagnetic signal recognition when applying existing deep learning networks. It studies classic deep learning network solutions for electromagnetic signal recognition both domestically and internationally, comparing and analyzing the strengths and weaknesses of various approaches. Subsequently, it proposes an electromagnetic signal classification method based on multi-channel feature extraction and dilated convolutional neural networks. By extracting the signal graph features, spectrum graph features, and double-layer CNN learned features input into a dilated dense convolutional network model for classification and recognition. By constructing and training a model on the RADAR dataset, the experiment achieved a high recognition rate. Additionally, through ablation experiments, the importance and effectiveness of each component of the model were validated. Finally, this paper discusses the limitations of the model in complex electromagnetic environments and the directions for future improvement.

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.

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.

Against the backdrop of duty patrols, the paper focuses on addressing the issue of patrol prevention and control path planning in a key area of a city, taking into account multiple factors such as road distance, terrorist attacks and harassment, and weather conditions. A Matlab simulation is used to generate a city area map, based on which the urban patrol path is planned using an improved ant colony algorithm. Comparative experiments are conducted to verify the feasibility, reliability, and efficiency of this improved algorithm in patrol path planning. The experimental results show that the improved ant colony algorithm has fewer iterations and takes much less time than other methods. It is also suitable for complex patrol route planning problems and can provide favorable technical support for patrol prevention and control.

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.

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.

The construction of SoS is a key and difficult content in the development plan of military construction. In response to the problem of continuous cost risk generation in the process of SoS construction cost investment, focusing on the mechanism and growth path of combat effectiveness generation, a SoS construction cost risk assessment method oriented towards “cost capability” balance is proposed. Firstly, define and analyze the concept of cost risk in SoS construction; Secondly, construct an evaluation model for the dependency and constraint relationships between factors such as SoS construction costs, capabilities, and risks; Finally, taking the construction of a SoS under task guidance as an example for case verification, the experimental results show that the proposed method can achieve cost risk assessment under the trade-off of "cost capability", and the selected MOPSO algorithm has strong robustness.

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.

In the field of equipment development project management, it is very important to accurately analyze the influencing factors of the whole process quality assessment to ensure the success of the project. In order to improve the effectiveness and reliability of project quality management, the analysis process must follow the principles of systematic comprehensiveness, objective science and quantification.By establishing a two-dimensional quality management coordinate system, the quality influencing factors are analyzed one by one from the argumentation and project approval stage, the scheme design stage, the prototype development stage, the test and evaluation stage, and the induction and type approval stage. Initially, 24 potential influencing factors were identified, and through further screening, 16 key quality influencing factors are ultimately determined. The adversarial interpretive structure model (AISM) has been used to structurize influence factors, build UP and DOWN hierarchical topologies, determine the interaction and hierarchical relationship between influence factors, and clarify the root layer influence factors, middle layer influence factors and superficial influence factors of equipment development projects.

In order to solve the quantity planning problem for the equipment system design, the “mission-capacity-type-model” four-equipment system construction method and process is presented based on the department of defense architecture framework. Secondly, the analytic hierarchy process is used to complete the weight distribution of each element of the equipment system. The equipment capability gap function is constructed based on the expected capability, and the calculation method of the expected and actual equipment capability is given. Then, the equipment quantity planning model with the minimization of the total capability gap of the equipment system as the optimization objective and the equipment quantity and investment as the constraint conditions is constructed, and the solution method is given based on the genetic algorithm. Finally, for a typical simulation example, the equipment system is designed and the quantity of each type of equipment is given, verifying the feasibility and effectiveness of the method.

Addressing the limitations of traditional battlefield equipment repair task scheduling models, such as the lack of adaptive learning capabilities and the subjective and experience-based determination of indicator weights, this study proposes a repair task scheduling model based on the GWO (Grey Wolf Optimization) algorithm combined with a BP neural network. First, a task scheduling indicator system comprising 11 indicators is constructed from three dimensions: repair tasks, repair teams, and battlefield environments; Second, the GWO algorithm is used to optimize the weights and thresholds of the BP neural network, avoiding getting stuck in local optima; finally, the network is trained using synthetic brigade exercise data to obtain the optimal model. Experimental results show that the GWO-BP model significantly reduces prediction errors compared to the BP model, enabling precise prioritization of repair tasks and providing an objective and efficient solution for battlefield equipment repair decision-making in synthetic brigades.

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.

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.

Blockchain technology operates on a distributed ledger and decentralized basis, making transaction information open to the public and untampered with. However, this feature makes it possible to leak the user’s identity information when the transaction information is disclosed, thus destroying the user’s anonymity. To this end, a blockchain data transmission protection method based on uBlock round function is proposed. Through the intelligent filling technology, the plaintext data within each block reaches the specific length required for the uBlock round function encryption. Based on this length requirement, a set of uBlock round functions with complex substitution and substitution transformation is designed. By performing multiple rounds of uBlock round function encryption operations, combined with an effective tamper prevention mechanism and a secure transmission protocol, the blockchain data is effectively transformed into an unpredictable random number sequence, ensuring the integrity and security of data transmission in the offline state on the basis of encryption. The experimental results show that under the application of the research method, the two test samples showed a higher security level in the protection of data privacy, both above 50 bits. This proves the great potential and practical application value of this research method in improving the security of offline transmission of blockchain data.