Submarine-borne laser weapons can improve defense capabilities and increase the types of mission that can be completed. By introducing the research progress of laser weapons, the development status and application situation of laser weapons in the US, submarine installation requirements is proposed. The paper suggests timing for weapon use and analyzes the ability to damage anti-submarine aircraft. And it analyzes the ability to damage anti-submarine aircraft, and Provides reference for relevant research.

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.

Facing the continuous improvement of war complexity under the trend of system-of-systems confrontation,this paper introduces MBSE to the modeling design of complex system-of-systems combat. Adopting Unified Architecture Framework, a modeling method based on scenes is proposed by sorting out the modeling needs and internal mechanisms of system operations. Taking the task that searching and attacking multiple target as an example,various kinds of view is constructed including strategic, operating, resources and services. Then, logic verification is carried out based on the model. This method can provide a reference for system-of-system combat design and UAF application.

The intending goal of modeling and simulation is to create a unified Live-Virtual-Constructive (LVC) integration architecture, support rapid integration model and carry out simulation for joint flight training, tactical coordination, operational planning and evaluation. By exploring the connotation and application field of LVC, this paper analyzes the development plan of LVC training network, combs three typical application examples of LVC system, which are distributed mission combat, joint simulation environment and US Navy simulation training. Using the experience of LVC to solve the joint training, this paper gives the enlightenment of accelerating the construction of the joint training of our army, and provides valuable reference for the construction of our army's joint training environment.

We are embracing the fourth wave of human development, which is a critical transition from the information society to an intelligent society integrating human beings, the physical world, and the cyberspace. In recent years, computing and information technology have developed rapidly. The unprecedented popularity and success of deep learning have established artificial intelligence (AI) as the frontier field of human exploration of machine intelligence. Meanwhile, thanks to the revolutionary progress of devices and the development of artificial intelligence (AI), brain-computer interface (BCI) implantation technology has also been rapidly implemented, which marks the beginning of the integration of BCI and AI, carbon-based and silicon-based. However, there are fundamental differences between the underlying logic of silicon-based and carbon-based computing, and the intelligent mechanism of the brain remains to be further explored. The visual cognition-guided twin AI deep network proposed in this study is a deep network technology driven by personal consciousness. It captures and analyzes individual thinking patterns and creative inspiration to create a unique visual world tailored for each user. In such an environment, everyone becomes the visual leader of their own created world, breaking the barriers between matter and consciousness, and expressing rich individuality and creativity.

The era of intelligent war is coming with the wide application of big data technology and artificial intelligence technology in military field. Based on the characteristics of intelligent war, this paper describes the challenges of traditional weapon equipment, and puts forward the development strategy of weapon equipment system under condition of intelligent war, it has certain practical significance to improve the system efficiency of weapon equipment and win the initiative of future wars.

The Russia-Ukraine conflict is the most widely used local conflict of unmanned intelligent equipment since its development and installation. The various types of unmanned intelligent equipment that emerged in this conflict have also brought a series of changes and challenges to the traditional killing chain. In order to study the combat application of unmanned intelligent equipment in the Russian-Ukrainian conflict and its impact on the killing chain, according to the public report information, the application and characteristics of unmanned intelligent equipment in the Russian-Ukrainian conflict are analyzed in three aspects from the perspective of killing chain: enriching the options of kill chain awareness nodes, reducing kill chain kill costs, and shortening kill chain closed-loop time. According to the experience and lessons learned from the application of unmanned intelligent equipment in Russia and Ukraine, the enlightenment and suggestions for the development of unmanned intelligent equipment are put forward.

Operational concept is an important manifestation of joint operation and an important driving force for the construction and application of new combat forces. In response to the rapid transformation of the war form, the innovation of operational concept based on solely writing cannot meet the needs of future operations. It is urgent to scientifically standardize the development of operational concept from an engineering perspective. On the basis of deeply analysis of the current research status of operational concept development at home and abroad, three typical requirements for describing, designing and verifying operational concept development are scientifically summarized. A operational concept development framework with four-layer and six-segment architecture is proposed and the main content, internal logic, basic processes, methods and tools of the development framework are analyzed in detail to provide a framework reference for developing specific operational concept.

Aiming at the situation of random changes in tactics and uncertain information in the confrontation pattern between missile and target in the air combat environment. By analyzing the motion relationship of a single missile attack to determine the target, the principles of dynamic game and differential game are introduced into the relative motion relationship between missile and target. In the dynamic game process of attack and defense confrontation, the terminal guidance problem of a single missile attacking and intercepting the target is modeled as a "one to one missile target" game model. The missile guidance law based on game theory under mixed strategy and the global strategic situation of bilateral optimization of the opponent at a certain moment are proposed. The method of model rolling prediction combined with differential game theory is introduced to simulate the confrontation between missile and target in uncertain attack and defense environments. The simulation results show that using this method can reduce the miss distance of missile to target and improve the hit accuracy of missile. The model provides a basis for missile attack and defense operations.

The development in artificial intelligence has dramatically changed all industries, among which AI-assisted air combat is a representative case of success. An Intelligent air combat model that consists of the attainment of samples and a decision-making model is constructed in connection with air combat simulator. Considering the characteristics of air combat continuous states and actions, DQN algorithm is selected as the model of intelligent air combat by comparison among several algorithms. Meanwhile, the AI network is trained interactively with AI enemies in the air combat simulation game DCS World, resulting in a model that is able to manipulate aircraft to a degree and many cases of air combat, by analyzing which a collection of winning, losing and dual samples is derived. The result of simulation indicates that the Intelligent air combat model has certain ability to generate strategic samples and enrich tactics in air combat environments.

Based on the formal description of the operational concept capability requirement analysis, a method of operational concept capability requirement analysis based on DRL(deep reinforcement learning) is designed. The key technologies of this method, such as simulation experiment, surrogate model, reinforcement learning, are analyzed and studied. Through the implementation of key technologies,small sample data sets with high reliability can be obtained through simulation experiments; Based on the experience data, the surrogate model of operation concept is constructed, and the model is optimized and trained by using multi-objective optimization algorithm with the high credibility simulation data set as the input; Finally, the surrogate model obtained from the training and the DRL framework are interactively optimized to achieve the reverse exploration of the operational concept capability requirements.

In modern military warfare, the pattern of air-ground coordination with multi-formation has become more and more important. However, the existing target intention recognition methods are effective for single formation, but lack of effective solutions for multi-formation scenarios with air and ground coordination. In this paper, Dynamic Series Bayesian Network is used to identify the intention of air-to-ground cooperative formation. This method firstly constructs an overall model of intention recognition of air-to-ground cooperative formation by using DSBN, which is used to describe the cooperative action process between air and ground formations. Then, events in different battlefield domains and their related probability relations are fused together with auxiliary battlefield information. The inference network is used to recognize the intention of enemy cooperative formation. This method fully considers the behavior rules of the air target, describes its behavior pattern and trend in detail, and is more suitable for the scenario of multi-cooperative target formation. Finally, the feasibility and effectiveness of this method is verified by simulation example.

The evaluation of intelligent combat effectiveness is an effective means for the demonstration and development planning of intelligent unmanned technology and equipment systems. Building a scientific and reasonable evaluation index system is the primary prerequisite for conducting intelligent combat effectiveness evaluation. The current status of technology for constructing operational effectiveness evaluation index systems are analyzed both domestically and internationally. A method for constructing intelligent operational effectiveness evaluation index systems that combines mechanism analysis and data mining is proposed. Key technologies such as the framework construction of intelligent operational effectiveness index systems and the measurement of cognitive domain index attributes are analyzed, providing theoretical reference for constructing a universal intelligent operational effectiveness evaluation index system.

With the widespread application of cruise missile technology and the increasing complexity of mission execution, the mission planning of cruise missile formations is facing new challenges. A multi patrol missile pre war planning task allocation model based on multiple constraints is constructed, with the optimization objectives of mission value return and mission loss cost, while considering various practical constraints such as task execution timing. To solve the model, an improved genetic algorithm based on simulated annealing is designed, which adjusts the adaptive selection strategy and adaptive crossover strategy based on the population fitness of different periods, ensuring convergence while increasing population diversity. Finally, the effectiveness of the designed improved algorithm in solving the task allocation problem of multiple patrol missiles was verified through a case study.

To solve the potential issue of reloading during mortar firing and ensure the safety of personnel and equipment during live firing in the military, a new type of mortar anti reloading system is designed. An indicator automatically switches its own state with the help of the sliding force during shell loading and buffering force during the movement of the buffering machine,undamentally solving the problem of "reloadingammunition" that may occur during mortar live firing. Through the interaction between the top rod component and the buffer machine, the buffering force can be transmitted to the anti reloading system. Through theoretical calculations and simulation experiments, it has been verified that its top rod component can meet the strength and stability requirements of mortar firing.

Aiming at the characteristics of large solution space, discrete, dynamic and nonlinear of firepower-target assignment problem, this paper proposes a deep reinforcement learning algorithm based on DQN. By combining the 6-layer fully connected feedforward neural network with the Q-learning algorithm, the perception ability of deep learning and the decision-making ability of reinforcement learning are fully utilized. Through the comparison of model performance tests, this method has strong fitting ability, fast convergence speed and small variance jitter, and the distribution results meet the combat expectations, which can provide some reference for commanders to make decisions on fire strike problems.

Aiming at the intelligent design of combat systems, we propose a conceptual framework and application methods of Agent based on artificial intelligence technology. First, we analyze the concept of Agent and discuss the important significance of studying Agent in the combat system. Then, we introduce the research framework of artificial intelligence based on Agent, and multiple application methods of Agent in combat systems. Finally, we analyze the development trends of Agent technology and the risks and challenges that its combat applications may face.

In response to the problem of limited evaluation indicators for the cost function of a single route planning algorithm, and the recommended optimal route for planning being the optimal local indicator, a land attack cruise missile route planning evaluation index system is established based on missile operational requirements and operational environment characteristics, starting from flight safety, penetration safety, and strike accuracy requirements. Qualitative analysis is conducted on the probability of missile hitting the ground and falling into the human zone, as well as the amplitude of trajectory crossing the detection and interception zones specific evaluation indicators such as autonomous flight and detection recognition accuracy have certain reference significance for optimizing route planning and optimization algorithms.

Two bearing-only maneuver detection methods based on deep learning are proposed to address the problems of long detection delay and low accuracy of existing bearing-only maneuver detection methods for underwater targets. The bearing observations of the target in the constant velocity (CV) motion state and constant turning (CT) motion state are used as the training data set. The target motion pattern classification and bearing prediction are realized through the Long short-term memory (LSTM) neural network, and then realize the maneuver detection of underwater targets based on motion pattern classification and bearing prediction. The simulation results show that compared with the traditional bearing prediction maneuver detection method, this method reduces the bearing observation error and has a lower sensitivity of target maneuver magnitude, and has a higher maneuver detection accuracy and reduces the maneuver detection delay.

Addressing the issue of relatively low recognition rates for named entities in domain-specific short texts under resource-constrained computational environments, a novel hybrid model combining a Dual BiLSTM_CRF architecture with a fully connected network is designed to identify named entities in these texts. The model leverages critical knowledge entities and their key relationships from a domain knowledge graph, which undergoes projection transformation, clustering, and global vector word embedding processing. Based on the calculation of word vector similarities, it identifies similar critical knowledge entities to those being recognized within the domain. These identified knowledge entities are then substituted into the original domain short text, generating an augmented version that is fed, along with the unmodified text, into the model for named entity recognition. This integration of domain knowledge into the recognition process of named entities in domain-specific short texts has shown promising results. Experimental outcomes demonstrate that this method outperforms existing comparable approaches in terms of its enhanced identification capabilities.

The main challenge of multi-object tracking (MOT) is identity switch caused by severe occlusion. The solution to identity switching is video object association, which assigns an identity number to the same target in different frames. In this paper, a long-short time association algorithm is proposed for identity switching. In the short-time, that is, the motion features between adjacent frames are used to match, and in the long-time, that is, the non-adjacent frames are directly added to the appearance features for association to rematch the object detected after occlusion. Besides, the Kalman filter is improved and the frame width parameter is added to make the predicted frame more accurate; appearance features use average appearance features and increase detection confidence as update parameters to make appearance more robust and can still work in complex scenes. The new tracker, LSATrack, achieves 81.3MOTA and 81.3IDF1 in the MOT17 and achieves stable tracking in severe occlusion scenarios.

Aiming at the lack of effective analysis and identification of operational area in the traditional battlefield situation awareness process, a method of aerial battlefield target trajectory analysis method for operational area extraction is proposed. Firstly, based on the improved Douglas-Peucker algorithm, the featured trajectory is extracted based on the original target trajectory, thus reducing the trajectory redundant information. Secondly, the clustering by fast search and find of density peaks (CFSFDP) algorithm is used to cluster the featured trajectory, which obtains multiple featured points clusters. Finally, each featured points cluster is scanned based on Graham's convex hull algorithm to obtain closed polygons to characterize the combat areas. The feasibility and effectiveness of the algorithm model are verified by several case simulations.

Geosynchronous orbit satellite is usually the first early warning equipment to catch ballistic missile launches in actual combat. However, the detection information it provides is lack of dimension, which makes the traditional target guidance method hard to be directly applied on the target guidance for low-orbit satellite. Aiming at this point, this paper studies the target guidance method for low-orbit satellite by geosynchronous orbit satellite. First, based on the altitude constraint of infrared radiation during the launch and active flight of the ballistic missile, the azimuth and pitch detection ranges of the infrared sensor of the low-orbit satellite are calculated with the guidance of one geosynchronous orbit satellite. Then, the detection distance of the infrared sensor of the low-orbit satellite is modeled and calculated, which leads to a successful target detection for the low-orbit satellite. Finally, the effectiveness and accuracy of this method are verified through a numerical simulation experiment.

Re-identification technology for pedestrians and vehicles has been successfully applied in the field of intelligence analysis. However, there is a lack of research on re-identification technology for ship targets. In this paper, we propose a double-feature fusion-based maritime defogging re-identification network for intelligence analysis and supervision of ship targets. To reduce the impact of negative samples on features, we adopt a perspective-assisted adaptive query expansion method and a similarity-based feature fusion method. Furthermore, a defogging branch is embedded in the shallow layer of the re-identification branch. This branch utilizes weight sharing technology to extract fog-free features. The defogged image is then reconstructed using upsampling technology and the pyramid model, enhancing the recognition ability of the re-identification network in low-visibility scenarios. Finally, a pseudo-IOU based non-maximum suppression method is proposed to enhance the detection accuracy of ship targets. This method modifies the confidence of the detection frame. Experimental results demonstrate that the proposed method outperforms existing methods, and each module contributes to the network’s performance.

The rapid development of artificial intelligence technology has influenced, constrained, guided, and even dominated modern combat methods. Especially in the field of command and control(C2), artificial intelligence technology deeply influences various elements and links of C2 activities through C2 systems and equipment. However, due to the novelty of artificial intelligence technology and the specificity of military systems, the impact of artificial intelligence on C2 is difficult to explicitly capture and measure during non war periods. This article systematically explores the mechanism of the role of artificial intelligence in the field of C2 from four aspects: the relationship between artificial intelligence and C2, the ways in which artificial intelligence affects the elements of C2, the trends in the impact of artificial intelligence on C2, and the risks and challenges faced by the co-evolution of artificial intelligence and C2. The conclusion drawn can assist military personnel in better controlling the battlefield, controlling war, and facing the challenges of war in the intelligent era.

In order to improve the recognition accuracy of radar targets and the recognition performance under incomplete angular domain data, a high resolution range profile (HRRP) recognition algorithm based on time series forest is proposed. The basic principle of time series forest algorithm and the basic steps of HRRP target recognition are introduced in detail. The experimental results of measured HRRP data show that compared with k-nearest neighbors(KNN), support vector machine(SVM), random forest(RF), convolutional neural network(CNN), long short-term memory(LSTM) and other classical target recognition algorithms, the proposed algorithm has better recognition performance and better angular domain promotion ability. Under the condition of training only 1/2 and 1/3 angular domain data, the average recognition rate of the full angular domain data is better than 85%, which is an average increase of 5.2% compared with the above method.

Aiming at the current situation of lack of environment and equipment in radar training, a radar operation training method based on the combination of virtual and real is proposed. Firstly, the connotation of the concept of virtual-real combination is analyzed. Secondly, the interaction methods of combining physical radar and virtual environment and virtual radar and physical environment are studied. Finally, combined with the specific training requirements, the application of real-real, real-virtual, virtual-real, and virtual-virtual radar interaction methods in radar training is analyzed in the same scenario.

In order to solve the target tracking problem of multiple formations in the context of unclear nascent target parameters, a formation target tracking algorithm with unknown nascent target-related parameters is proposed, which can effectively improve the tracking performance and thus better meet the demand. By using the Gaussian mixed probability hypothesis density (ET-GM-PHD) filtering framework, the PHD parameters are predicted and then the correlation between each Gaussian component is analyzed to filter out the optimal set of measurements and finally eliminate most of the noise disturbances. Based on the characteristic of dense distribution measured in the movement area of the formation target, the location of the formation target is found and tracked by the target detection method. Finally, the simulation experiments show that the method in this paper can track the formation target quickly and effectively with good tracking performance when the parameters related to the nascent target are unknown.

Target maneuver type recognition is a key aspect in the fields such as object tracking and intention recognition. A multi-layer label generation method based on improved K-medoids clustering is proposed to address the problems of insufficient universality and low accuracy of existing maneuver type recognition methods. Taking the maneuver trajectory of aerial targets as an example, various data preprocessing methods are designed, and dynamic time warping is used as the distance metric for clustering algorithm. By constructing a label framework to guide the implementation process of the clustering algorithm, iterative clustering is carried out to generate multi-layer labels. The algorithm is tested on a public dataset, and the experimental results showed that the recognition accuracy of the first layer label achieved 89.75% under unsupervised conditions, which is closed to traditional supervised algorithms; Meanwhile, compared to the condition without introducing a label framework, it is more effective in distinguishing the second layer of fuzzy labels.This method requires only a small amount of expert knowledge and can be easily extended to different fields and different maneuver type.

"Demand traction, technology promotion" is the main rule of the development of equipment system construction. This paper focuses on the analysis of system requirements, and puts forward the process of system requirements analysis on the basis of analyzing the basic connotation of system requirements analysis, combining the advantages of threat and capability; aiming at the key technical problems such as capability gap measurement and mapping analysis in system requirements analysis. The capacity gap measurement model is established, and the QFD method is used to realize the mapping between the capability requirement-system requirement-technology requirement, and the analysis of the typical space target monitoring system is taken as an example to verify the system demand analysis method proposed in this paper, which provides a theoretical basis for carrying out the system demand analysis.

The network topology is marked with the device type, which is difficult to reflect the service relationship between the device nodes. Aiming at this problem, this paper proposes a network service node division algorithm based on service mapping. The algorithm judges the ownership of the service community through the service mapping relationship between network nodes, and finds the key nodes of network services and the subordinate relationship between nodes. This paper introduces virtual community based on traditional membership matrix. The network service community membership influence degree algorithm not only give the community membership degree of nodes, but also reflect the global influence degree of nodes, providing a basis for the selection of key network nodes in network security. In the experiment based on LFR network, this paper compares the algorithm sensitivity and performance with similar algorithms, and validates the effectiveness of algorithm partitioning in the Internet2 OS3E simulation experiment by simulating denial of service attacks. The comparative experimental results show that the service mapping algorithm has the characteristics of low sensitivity and high practicability in threshold selection, and its performance is better than that of similar algorithms. The simulation results show that the service community partition is effective and can give the correlation between node services.

As a key factor to determine the form of future war, human-machine fusion has attracted the attention of experts in the military field of various countries from its inception to its rise, and it is a necessary means to promote the cooperation between human intelligence and machine intelligence. In this paper, the current research status, relevant important concepts and practical applications of human-machine fusion systems are introduced. Then, the development from unmanned systems to human-machine fusion is analyzed. Moreover, the relevant theories and cooperation strategies of human-computer fusion are summarized. The implementation framework, collaborative mechanism, and development analysis are obtained. The performance optimization methods and key technologies of collaborative optimization are deeply analyzed.

In order to solve the problem of scientificity and efficiency of force grouping in joint operational planning, a nonlinear method of generating polymeric operational capability from combative element capability is proposed, and a force grouping relationship of "operational subtasks to polymeric operational capability to combative meta-capabilities to battalion combative units" is established. And a modular force grouping model at the joint operational level with multiple optimized targets and multiple constraints is constructed based on the matching of supplying and demanded vectors of operational capability. A model solving framework based on genetic algorithm is proposed, and the processes and methods of coding decided variable, calculating adaptive value and decoding chromosome of the force grouping are explained. Experiments show that the modular force grouping model based on the matching of operational capability can accurately and efficiently group battalion combative units to meet the needs of operational tasks.

Blockchain technology has several characteristics, including decentralisation, non-tampering, and cryptographic algorithms. These characteristics are the focus of research for distributed group network authentication. However, the existing blockchain has high arithmetic requirements and low efficiency, which limits its application in UAV swarm group networks. Therefore, a lightweight blockchain UAV swarm identity authentication system has been designed. A model for a blockchain-based authentication system for UAV swarms is constructed. The system’s blockchain consensus algorithm, Practical Byzantine Fault Tolerance (PBFT), has been improved. A new lightweight trusted PBFT consensus algorithm, Lightweight Trusted PBFT (LTPBFT), has been proposed. This algorithm reduces the reputation reward and punishment mechanism and the voting mechanism by introducing the probability of Byzantine nodes acting as master nodes. The algorithm achieves this by introducing a reputation reward and punishment mechanism and a voting mechanism. The consistency protocol process has been optimised to simplify both its Prepare and Commit phases. This reduces communication complexity and achieves a lightweight authentication system. Simulation results demonstrate that the LTPBFT consensus algorithm is more efficient and stable than PBFT in terms of communication overhead, throughput, consensus delay and security.

Aiming at the demand for accurate damage assessment of building targets, a new damage assessment method based on finite element simulation results recognition is proposed. The structural dynamic finite element analysis software SAP-2000 is used for numerical simulation and analysis of target damage, and the pre-assessment of target damage before attack is realized by numerical simulation images feature recognition and quantization combined with the target functional and physical damage level discrimination criteria. The rationality and availability of the method are verified by the simulation of examples.

This article adopts a combination of Analytic Hierarchy Process (AHP) and Ideal Point Method (IPM) to construct a decision-making model for ammunition supply methods, which includes six influencing factors: operational process time dimension, battlefield advance distance, task importance, task urgency, possibility of resource allocation around and ammunition demand. Firstly, we select four typical ammunition supply methods: fixed point supply, mobile supply, adjustment supply, and accompanying supply, and determine the decision-making attributes of the ideal situation. Then, we calculate the weights of each influencing factor through expert evaluation and scoring. Finally, based on the numerical calculation of each influencing factor given by various experts on the battlefield and the closeness of each ideal wartime ammunition supply method, the one with the highest closeness is the optimal wartime ammunition supply method. Through case analysis, the effectiveness of this method is verified, which has guiding significance for the decision-making of ammunition supply methods in actual wartime, and provides methods and ideas for in-depth research on ammunition supply decision-making.

In order to improve the search ability of carrier-based early warning aircraft to enemy surface ships and ensure the safe and continuous tracking, monitoring and early warning of carrier-based early warning aircraft, a method for reconnaissance and early warning position configuration of carrier-based early warning aircraft for sea surface targets is proposed. Based on the basic requirements of its position configuration and the conditions that meet the combat background, the factors affecting the early warning distance of the carrier formation provided by the carrier-based early warning aircraft are analyzed, and the patrol line configuration model of the carrier-based early warning aircraft is established. The situation of the carrier-based early warning aircraft at the end of the patrol line is fully considered, and the minimum passive detection distance is calculated. According to the position and motion state of the enemy attacking the surface ship and its combat capability, the quantitative research on the position configuration of our carrier-based early warning aircraft is carried out. The effectiveness of the model is verified by Matlab simulation experiments, and suggestions are made for the position configuration of the carrier-based early warning aircraft to meet the needs of combat use.

Aiming at the importance of the infocommunication wargame as a research platform to improve commanders’ command and planning of infocommunication support, the infocommunication wargame counters are chosen for modelling studies as an important component. Firstly, the modeling framework of infocommunication wargame counters is designed, the modeling steps of entity decomposition, attribute extraction, data description and corresponding products are clarified. Then a pre-categorization ontology-based approach for modelling infocommunication wargame counters is proposed, defining the base classification of entities, the ontology models of different entities and the computer language description of the counters. Finally, with the help of the ontology modelling software Protégé, an experimental analysis is carried out, using the example of infocommunication support in island offensive battles, verifying the feasibility and scientificity of this method.

Marine battlefield environment is the important condition that restricts the effectiveness of maritime weapons and the conduct of military operations. Accurate assessment of environmental impact is the "soft power" for improving maritime combat capability and battlefield construction. Firstly, this paper briefly introduces the marine battlefield environment and its impact assessment. Then, the assessment methods and modeling techniques of battlefield environment are summarized into four categories: dynamic simulation-based assessment method, decision science-based assessment method, data science-based assessment method and uncertain artificial intelligence-based assessment methods, and the above methods are described in detail. Finally, the different methods are compared and the application prospect is analyzed.

Aiming at the problem of error in deep-sea DOA(Direction of Arrival, DOA) estimation based on plane wave sound field model, this paper establishes the array signal model of deep-sea DOA estimation under multipath channel based on ray theory. The characterization method of deep-sea multipath ray propagation time and adjacent array element ray propagation delay difference are also derived. Compared with the propagation delay difference expression of adjacent array elements in the traditional plane wave model, the propagation delay difference expression of adjacent array elements derived in this paper is more universal. Combined with the decoherence DOA estimation algorithm, the performance of deep-sea DOA estimation is improved, and the effectiveness of the algorithm is verified by simulation. The research shows that the time delay difference between adjacent array elements is not only related to the distance between array elements and the target azimuth, but also related to the depth of the sound source and the horizontal distance from the sound source to each array element. The construction of the steering vector in DOA estimation should consider the influence of these factors. At this time, DOA estimation is a multi-dimensional parameter optimization problem. Fully considering the acoustic propagation characteristics of the ocean sound field can fundamentally solve the error problem of deep-sea DOA estimation.