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.

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

The 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.

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.

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.

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.

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.

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.

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.

"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.

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.

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.

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.

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.

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 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.

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.

This research addresses the crucial problem of collision avoidance decision making for autonomous ships under diverse encounter situations. Building upon the Deep Deterministic Policy Gradient (DDPG) algorithm, appropriate reward functions based on the International Regulations for Preventing Collisions at Sea (COLREGS) have been designed to effectively guide intelligent agents in acquiring optimal strategies. By incorporating the concept of potential reward shaping, the proposed approach ensures efficient obstacle avoidance while adhering strictly to the established rules. Moreover, extensive simulations have been conducted to validate the algorithm’s performance in collision avoidance for both dual-ship and multi-ship scenarios under varying encounter situations, and a comparative analysis with the TD3 algorithm has been undertaken. The obtained results demonstrate that the proposed algorithm exhibits rapid convergence and stable training performance. The resulting models successfully achieve collision-free navigation while strictly adhering to the COLREGS. Particularly, in two-ship encounter situations, the proposed algorithm outperforms the trajectory planned by the TD3 algorithm in terms of shorter path length and higher efficiency.

This study explores technical methods based on light vision to address the problem of target detection and tracking by surface unmanned ships in complex environments. We utilize an improved dark channel dehazing method and guided filtering for image preprocessing to improve the accuracy and efficiency of subsequent image processing. In terms of target detection, the YOLOv7 algorithm is used, which effectively improves the accuracy and recall rate of target detection by optimizing the loss function. In order to achieve accurate multi-target tracking, combined with self-trained model weights and Sort algorithm, continuous tracking of targets and accurate annotation of center point trajectories are successfully implemented. In addition, a binocular camera system is built on an unmanned ship platform for target ranging. Experimental results show that our method can achieve the ranging function with an average relative error of 6.46%. This result not only improves the navigation and positioning capabilities of unmanned ships, but also provides technical support for water surface safety monitoring. This research demonstrates that in the field of surface unmanned ships, target detection and tracking problems can be effectively solved by integrating advanced image processing technology and machine learning algorithms.

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.

Intelligent game adversarial algorithms not only make full use of the portrayal accuracy of the game model, but also solve the equilibrium solution through the powerful computational ability of neural network and the trial-and-error mechanism of reinforcement learning, which makes the intelligent game adversarial algorithms achieve good results in many fields. Through the multi-intelligence body game learning, multi-intelligence body game reinforcement learning and multi-intelligence body game deep reinforcement learning three levels of intelligent game confrontation algorithm is systematically sorted out, and the corresponding mapping with the intelligence field of work, demonstrates the feasibility and necessity of intelligent game confrontation algorithm in the field of intelligence, and finally gives the specific application of the intelligent game confrontation algorithm in the field of intelligence and the effective measures of the follow-up to improve the quality and efficiency. Finally, it gives the specific application of intelligent game confrontation algorithm in the field of intelligence, as well as the effective measures to improve the quality and efficiency.

Improving the training effect of multi-agent has always been the focus in the field of reinforcement learning. Based on the multi-Agent twin-delay deep deterministic policy gradient (MATD3) algorithm, a parameter sharing mechanism is introduced to improve training efficiency. At the same time, in order to alleviate the inconsistency between real rewards and auxiliary rewards, drawing on the ideas of course learning, a decay factor for auxiliary rewards is proposed to ensure the motivation of policy exploration in the early training period and the reward consistency in the late training period. And the proposed improved MATD3 algorithm is applied to combat vehicle games to achieve intelligent decision-making of the vehicle. The application results show that the reward curve of the vehicle converges stably and the effect is good. Besides, the improved algorithm is compared with the original MATD3 algorithm, and the simulation results verify that the improved algorithm can effectively improve the effect of convergence and the convergence value of reward.

In order to efficiently and correctly identify abnormal behaviors in Linux logs, this paper proposes a Linux log anomaly detection method based on the improved isolated forest algorithm. The method introduces an attention mechanism on the basis of the isolated forest algorithm, which can dynamically adjust the attention features and sample points when processing log data, and dynamically adjust the degree of attention according to the degree of abnormality of the samples. Experimental results show that the method achieves high efficiency in the Linux log anomaly detection task compared with traditional methods, and can effectively discover potential security threats and abnormal behaviors.

In order to achieve real-time stable tracking of moving targets and improve the accuracy and success rate of the tracking system, a kernel correlation filtering-based target tracking method with scale adaptation and feature fusion is proposed to address the situation that the traditional kernel correlation filtering algorithm does not track well when the target is obscured or motion blurred. Firstly, in the feature extraction process, color features are added after the original directional gradient histogram features to improve the recognition capability of target features, that is HOG features are fused with CN features, then a scale pyramid is constructed to perform scale estimation to achieve scale adaptation of the target, and finally the model is updated through a multi-peak detection mechanism. Through testing on the OTB2015 dataset, the accuracy and success rate of the algorithm has been further improved, and the algorithm is able to accurately identify targets and track them effectively.

Arming at the coexistence dilemma of inventory overhang and shortage of different maintenance equipment supply nodes, a steady-state coefficient of a supply node has been described with which contains inventory level, the support degree between nodes each other, the demand status of the area guaranteed by the node and the cost of risk when the demand cannot be met. The steady-state coefficient of the inventory network was calculated on the base of all of support nodes. The active adjustment is carried out flowing the corresponding algorithm according to the gap between the steady-state coefficient and the threshold which has been set before. The scientific, automatic and intelligent level of equipment adjustment can be greatly improved, consequently the success rate of maintenance support is improved if the passive stock adjustment is combined because of out of stock at the same time.

In order to further solve the optimization problem of reconnaissance constellation and strengthen the construction of space-based reconnaissance system, the current optimization methods of reconnaissance constellation are studied and summarized. The design and optimization of reconnaissance constellation has the characteristics of multi-parameter, multi-objective, nonlinear and discontinuous, which is a typical multi-objective optimization problem. Multi-objective evolutionary algorithm is a kind of biological intelligence algorithm based on population, which carries out probabilistic search in decision space according to predetermined heuristic rules. It does not need continuity, differentiability and other conditions of the problem, and can get a group of solution sets close to Pareto frontier within a limited number of search times. It can effectively solve the multi-objective optimization problem and has been widely used in the multi-objective optimization of reconnaissance constellation. This paper discusses the construction of the reconnaissance constellation optimization model and the classification, advantages and disadvantages of the multi-objective evolutionary algorithm, discusses the application of the multi-objective evolutionary algorithm in the reconnaissance constellation optimization, and points out the development direction of the reconnaissance constellation optimization based on the multi-objective evolutionary algorithm.

Due to the low signal-to-noise ratio of raw data, the reliability of data and the accuracy of acoustic source localization are severely affected by fiber optic acoustic sensing technology. To address this issue, this study optimizes the wavelet thresholding method. Firstly, a novel thresholding function is proposed, which achieves denoising while preserving key information through shape adjustment factors. It combines the advantages of both hard and soft threshold functions and has high flexibility and controllability. Secondly, an adaptive threshold calculation method is introduced, utilizing an improved simulated annealing algorithm to optimize threshold selection, reducing the algorithm’s dependence on threshold parameter selection. Through simulation experiments, it has been verified that this research method effectively suppresses noise in the signal and improves data availability. Compared to the original methods, this approach significantly improves the signal-to-noise ratio and demonstrates robustness in simulated tests of real signals.

This paper presents a method of equipment demonstration AoA method considering RMS factors. It puts forward the equipment demonstration AoA framework, and elaborates equipment demonstration AoA measure system. It analyses the RMS and cost factors, and establishes their quantitative models. An AoA model based on Data Envelopment Analysis (DEA) and an application example are given to illustrate the feasibility and effectiveness of the method.

To solve the problem of low recognition accuracy of radar active jamming in strong noise environment,an algorithm for ER-C-L(Extended ResNet-CNN-LSTM) network model based on one-dimensional composite features is proposed. Firstly, the amplitude, instantaneous frequency, instantaneous envelope of power spectrum and their composite features are taken as network input to compare their recognition accuracy in ResNet-CNN model. The composite features of amplitude and instantaneous envelope of power spectrum with high detection probability and small data volume are selected as the optimal features. Then, the complex features are injammed into the ER-C-L network to identify six new active jamming models. Simulation experiments show that the recognition accuracy of jamming is 98.5% in strong noise environment within the JNR of -10 dB. Compared with other deep learning algorithms such as CNN, ResNet-CNN, extended ResNet-CNN and LSTM, it has higher interference recognition accuracy.

Underwater multicell single-layer thin metal plate corner reflectors can’t form a relatively stable TS in a large space because of its strong frequency characteristics. To solve the problem, the paper designs the underwater multicell cavity corner reflectors. Then using FEM coupled DBEM solver to simulated its scattering acoustic field in incident wave frequence varies from 5.0 kHz to 15.0 kHz. The results show that the underwater multicell cavity corner reflectors has weak frequency characteristics, obvious decoupling effect and strong scattering ability. Underwater multicell cavity corner reflectors can maintain stable target strength in a large space, and has better reflection ability, so it is an ideal underwater acoustic reflection device.

By analyzing the characteristics of collaborative decision-making behavior of heterogeneous clusters, this paper introduces quantum decision-making model to solve the autonomous decision-making problem of heterogeneous clusters. Firstly, it is triggered from the OODA loop process to clarify the control mode of heterogeneous cluster collaboration. Then, aiming at the specific problem of heterogeneous cluster cooperative cruise, the tasks of unmanned boat cluster and UAV cluster are split, and the application models of formation optimization and autonomous detection are constructed respectively. The formation optimization algorithm is designed to dynamically generate the best communication formation structure. The quantum decision cloud and feedback scoring mechanism are designed to select the most suitable decision content among many possible detection options. Finally, the effectiveness of formation optimization model and autonomous detection model in heterogeneous cluster cooperative cruise is demonstrated by simulation experiments. Experiments show that the quantum decision model has greater applicability and mobility in decision behavior selection and optimization than the conventional decision model.

Aiming at the task assignment problem of heterogeneous UAV performing multi-type fixed tasks on the ground, a task allocation method based on Cooperative Multiple Task Allocation Problem (CMTAP) and Modified Chimp Optimization Algorithm (MChOA) is proposed. Firstly, the CMTAP model is established, and the combat scene is depicted by setting the elements in the quad, and a multi-objective evaluation function with the highest total return, the lowest threat cost, and the shortest total flight range is established. Secondly, the MChOA algorithm is used to complete the model solving: the chaotic reverse learning strategy is adopted to improve the diversity of the intial population distribution. In the iteration stage, the global and local search capabilities of the algorithm are better balanced by adopting the dynamic adjustment strategy of preemptive behavior. Comparative experiments show that compared with the original algorithm, the resulting task allocation scheme reduces the average total voyage in the two scale scenarios by 22.6% and 21.1%, respectively, while maintaining a higher task gain and a lower threat cost.

The calculation of target distribution area is a prerequisite for anti-ship missile tactical decision-making and target acquisition probability calculation. On the basis of studying the target pursuit model of anti-ship missile, based on the relaxation variable method, the calculation equation for the target distribution is established, sufficient and necessary conditions are proved, the analytical model of target distribution area is obtained. Under the assumption that the target movement direction is arbitrary, and the calculation form under general conditions is given. Through the simulation example, the variation of distribution area under different conditions is compared, which can provide intuitive reference and estimation basis for the decision-making and use of the battle commanders.

A scheduling method based on equipment load balancing rules is proposed to solve the problem of unbalanced load of each observing device in traditional space target monitoring resource scheduling. In this paper, the visibility of space targets is taken as the mathematical basis, STK software package is used to forecast the visibility of tracking arcs of space targets, the equipment load balancing scheduling rules are integrated into the traditional scheduling process, and the observation requirements are respectively 3 laps, 4 laps and 5 laps as the input for experimental simulation. The generated scheduling scheme is presented in the form of Gantt chart. Finally, the constructed evaluation index is used to evaluate the value. The experimental results show that the scheduling rules based on equipment load balancing can well achieve the balance of the working time of each observation equipment, and can realize the tracking and monitoring of more space targets.

In order to improve the probability of underwater target capture, the double-mine parallel heading salvo method has been widely adopted, and the strike advance angle, deployment coefficient and spread angle of the salvo model all affect the target capture probability, and previous research in related fields mainly focuses on the influence of double mine deployment coefficient and spread scatter angle on target capture probability, which has certain limitations. In this paper, a parallel heading salvo model to improve the probability of double mine capture is given, and a multi-parameter optimal solution equation is theoretically derived, and a multi-parameter adaptive optimization method is given to solve the equation. Combined with simulation experiments, based on Monte Carlo method, the feasibility of multi-parameter adaptive optimization method is verified by comparing the target capture probability of the traditional double-ray salvo model with the optimization model. The results of the study provide an effective reference for the further use of torpedo salvo tactics.

A modified range parameterized instrumental variable pseudo linear Kalman filter (MRP-IVPLKF) algorithm is proposed for single station bearings-only ground maneuvering target tracking. Firstly, based on the detection range of the observation station, the relative range between the target and the observation station is divided into several sub-intervals using range parameterization, and instrumental variable pseudo linear Kalman filters are independently operated. Then, based on the filtering innovation and innovation covariance, the sub-filter weights are updated and target maneuvering is detected, ensuring filter stability by resetting the sub-filter weights and state information. Finally, the state information of each sub-filter is weighted and fused to obtain the target state, solving the problem of the decrease in tracking accuracy caused by unknown initial range and target maneuvering. The simulation results show that the position tracking accuracy of the algorithm proposed in this article is significantly improved compared to traditional methods, and can effectively achieve tracking of maneuvering targets.

In order to mine the key nodes in the spatial information network in the real task scenario and protect them,based on the mission tasks undertaken by spatial information network in the context of system, node types and information flow relationships are distinguished with the help of operational ring theory. Considering the high dynamic nature of spatial information network, time-varying graph analysis method is used to model the network. Based on the spatial information dynamic network model, the value factor of the operational ring is given by considering the formation time of the operational ring and combining with the traditional complex network indicators, the importance evaluation index of the spatial information network nodes is proposed as time-varying ring betweenness.The effectiveness and rationality of the proposed method are verified by comparing the node importance under indexes of time-varying ring betweenness, degree centrality and number of operational rings through experimental simulation.

To confront the growing difficulty along with the increasing complexity and comprehensiveness of airborne system, an airborne system integrating architecture for multiple types of models are proposed. A platform based on virtual data bus and adapters is developed. The platform’s accuracy for single model simulation is tested compared to the model’s original platform using the wind speed model of a real airplane. The platform’s integration ability, a display and control joint model containing PFD, MFD, ND, engine status collecting device, wind speed model and visualization software is established. The result shows this platform is able to efficiently integrate multiple types of models and is capable of supporting the development of complex airborne systems.

This paper focuses on the task allocation and path planning problem for aircraft material delivery, considering two complexity factors: aircraft loss caused by malicious interference and delivery timeliness. It establishes an objective function to be optimized, which includes aircraft loss, total mileage, and untimely supply rate. Meanwhile, it proposes an optimization approach based on prior knowledge such as "task merging" and "saturation supply", and implements an improved genetic algorithm. Simulation results show that the improved genetic algorithm with fused prior knowledge addresses the slow convergence problem caused by a large search space, improving the solution speed and optimization effect of the model.