Model architecture-oriented combat system effectiveness simulation based on MDE

Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.

Modeling of Naval Vessels Combat System based on Complex Network

Naval Vessels Combat System is a kind of complex system.The modeling of combat system has become hot issues in the past years.This paper proposed a new method to establish models of combat system based on the theory of Complex Network.The method of modeling considered the operational entities as nodes.It considered flow of information,substance and energy as edges in a network.The research also carries on a simulation to prove the applicability.Ultimately,the paper concluded that this method is applicable and accurate.

The personal protective equipment(PPE)based on individual combat:A systematic review and trend analysis

With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.

Mastering air combat game with deep reinforcement learning

Reinforcement learning has been applied to air combat problems in recent years,and the idea of curriculum learning is often used for reinforcement learning,but traditional curriculum learning suffers from the problem of plasticity loss in neural networks.Plasticity loss is the difficulty of learning new knowledge after the network has converged.To this end,we propose a motivational curriculum learning distributed proximal policy optimization(MCLDPPO)algorithm,through which trained agents can significantly outperform the predictive game tree and mainstream reinforcement learning methods.The motivational curriculum learning is designed to help the agent gradually improve its combat ability by observing the agent's unsatisfactory performance and providing appropriate rewards as a guide.Furthermore,a complete tactical maneuver is encapsulated based on the existing air combat knowledge,and through the flexible use of these maneuvers,some tactics beyond human knowledge can be realized.In addition,we designed an interruption mechanism for the agent to increase the frequency of decisionmaking when the agent faces an emergency.When the number of threats received by the agent changes,the current action is interrupted in order to reacquire observations and make decisions again.Using the interruption mechanism can significantly improve the performance of the agent.To simulate actual air combat better,we use digital twin technology to simulate real air battles and propose a parallel battlefield mechanism that can run multiple simulation environments simultaneously,effectively improving data throughput.The experimental results demonstrate that the agent can fully utilize the situational information to make reasonable decisions and provide tactical adaptation in the air combat,verifying the effectiveness of the algorithmic framework proposed in this paper.

Optimal confrontation position selecting games model and its application to one-on-one air combat

In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.

A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

Combating Cholera

Lying in her makeshift hospital bed,Joyce Tembo thanked medical personnel for evacuating her to the designated national cholera treatment centre,6 km north of Zambia’s capital Lusaka.She was recently diagnosed with diarrhoeal disease.Tembo,43,commended the medical sta!stationed at the treatment centre for their great service to thousands of patients,especially women and children seeking urgent treatment.“I am very grateful to the Chinese doctors who attended to me as soon as the ambulance rushed me to the clinic where I received urgent treatment;they have really saved my life,”Tembo told ChinAfrica.But not all residents in her community are as lucky as her.Many in the densely populated slums die every day due to the area’s poor sanitation-one of the major causes of the cholera outbreak.

OPTIMIZATION FOR COMBAT CONFIGURATION OF AIR DEFENSE WEAPON SYSTEMS

At evaluating the combat effectiveness of the defense system, target′s probability to penetrate the defended area is a primary care taking index. In this paper, stochastic model to compete the probability that target penetrates the defended area along any flight path is established by the state analysis and statistical equilibrium analysis of stochastic service system theory. The simulated annealing algorithm is an enlightening random search method based on Monte Carlo recursion, and it can find global optimal solution by simulating annealing process. Combining stochastic model to compete the probability and simulated annealing algorithm, this paper establishes the method to solve problem quantitatively about combat configuration optimization of weapon systems. The calculated result shows that the perfect configuration for fire cells of the weapon is fast found by using this method, and this quantificational method for combat configuration is faster and more scientific than previous one based on principle via map fire field.

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.

Research on system-of-systems combat simulation model formal specification and representation

To makesystem-of-systems combat simulation models easy to be developed and reused, simulation model formal specification and representation are researched. According to the view of system-of-systems combat simulation, and based on DEVS, the simulation model＇s fundamental formalisms are explored. It includes entity model, system-of-systems model and experiment model. It also presents rigorous formal specification. XML data exchange standard is combined to design the XML based language, SCSL, to support simulation model representation. The corresponding relationship between SCSL and simulation model formalism is discussed and the syntax and semantics of elements in SCSL are detailed. Based on simulation model formal specification, the abstract simulation algorithm is given and SCSL virtual machine, which is capable of automatically interpreting and executing simulation model represented by SCSL, is designed. Finally an application case is presented, which can show the validation of the theory and verification of SCSL.

Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.

Autonomous air combat decision-making of UAV based on parallel self-play reinforcement learning

Aiming at addressing the problem of manoeuvring decision-making in UAV air combat,this study establishes a one-to-one air combat model,defines missile attack areas,and uses the non-deterministic policy Soft-Actor-Critic(SAC)algorithm in deep reinforcement learning to construct a decision model to realize the manoeuvring process.At the same time,the complexity of the proposed algorithm is calculated,and the stability of the closed-loop system of air combat decision-making controlled by neural network is analysed by the Lyapunov function.This study defines the UAV air combat process as a gaming process and proposes a Parallel Self-Play training SAC algorithm(PSP-SAC)to improve the generalisation performance of UAV control decisions.Simulation results have shown that the proposed algorithm can realize sample sharing and policy sharing in multiple combat environments and can significantly improve the generalisation ability of the model compared to independent training.

A Space Unrestricted Multi-Robot Combat Internet of Things System

Multi-robot cooperation problem has received increasing attention in the research community and has been extensively studied from different aspects. Space constrain problem is a major issue for building a multi-robot system. This con- strain is a major hindrance for the efficient cooperation among robots in multi-robot applications. In this paper, we demonstrate a novel architecture of a multi-robot system without space restriction. Our architecture is based on the Internet of Things technology. We validated the proposed architecture using a case study considering a multi-robot combat application.

Network-based structure optimization method of the anti-aircraft system

The anti-aircraft system plays an irreplaceable role in modern combat. An anti-aircraft system consists of various types of functional entities interacting to destroy the hostile aircraft moving in high speed. The connecting structure of combat entities in it is of great importance for supporting the normal process of the system. In this paper, we explore the optimizing strategy of the structure of the anti-aircraft network by establishing extra communication channels between the combat entities.Firstly, the thought of combat network model(CNM) is borrowed to model the anti-aircraft system as a heterogeneous network. Secondly, the optimization objectives are determined as the survivability and the accuracy of the system. To specify these objectives, the information chain and accuracy chain are constructed based on CNM. The causal strength(CAST) logic and influence network(IN) are introduced to illustrate the establishment of the accuracy chain. Thirdly, the optimization constraints are discussed and set in three aspects: time, connection feasibility and budget. The time constraint network(TCN) is introduced to construct the timing chain and help to detect the timing consistency. Then, the process of the multi-objective optimization of the structure of the anti-aircraft system is designed.Finally, a simulation is conducted to prove the effectiveness and feasibility of the proposed method. Non-dominated sorting based genetic algorithm-Ⅱ(NSGA2) is used to solve the multiobjective optimization problem and two other algorithms including non-dominated sorting based genetic algorithm-Ⅲ(NSGA3)and strength Pareto evolutionary algorithm-Ⅱ(SPEA2) are employed as comparisons. The deciders and system builders can make the anti-aircraft system improved in the survivability and accuracy in the combat reality.

Entry-Level Forward Surgical Team Training Is Associated with Increased Confidence of Primary Combat Surgeons

Background: In recent years, we have established an entry-level Forward Surgical Team (FST) training program in a Chinese military medical university for the 5th grade undergraduates, who would be deployed to different military medical services as primary combat surgeons. This study aimed to assess the role of this pre-service training in improving their confidence with combat medical skills, after several years since they received the training. Methods: We conducted a nationwide survey of 239 primary combat surgeons who have ever participated in an entry-level FST training program before deployment between June 2016 and June 2020, which was for evaluating on a 5-point Likert scale the benefits of entry-level FST training and conventional surgery training in improving their confidence with combat medical skills. The difference in scores was compared using the student t-test. Significance was considered as P Results: The total score was significantly higher for entry-level FST training than that for conventional surgery training (30.76 ± 4.33 vs. 28.95 ± 4.80, P There was no significant difference between the training for surgical skills confidence scores (18.03 ± 8.04 vs. 17.51 ± 8.30, P = 0.098), but for non-technical skills, the score of entry-level FST training was significantly higher than that of conventional surgery training (12.73 ± 5.39 vs. 11.44 ± 5.62, P The distributions of confidence scores were different under various subgroups by demographics. There were no significant differences in scores between the two training in all specific surgical skill sets except “life-saving surgery” (P = 0.011). Scores of all 4 non-technical skill sets were significantly higher for entry-level FST than those for conventional surgery training (P Conclusions: The training should be considered as an essential strategy to improve confidence in combat medical skills, especially life-saving surgery and non-technical skills, for primary combat surgeons.

基于“马赛克战”的水下有人-无人作战使用探讨

通过对“马赛克战”概念的背景和特点进行分析,提出基于马赛克的水下攻防作战运用构想,构建基于马赛克的水下有人-无人作战网络,分析多域背景下水下OODA作战网络的构成,利用OODA作战环理论对水下马赛克作战网络作战能力进行分析,并提出水下马赛克作战网络对抗单个平台和对抗作战网络的使用方法。最后,从通信组网、集群使用、人工智能、察打一体等4个方面对“马赛克战”概念下水下无人航行器装备使用提出建议。

无人机自主作战效能评估技术研究综述

无人机自主作战效能评估是衡量无人机在特定作战条件下能否运用自主能力完成预定目标任务的有效途径,是无人机自主作战技术论证和装备体系建设发展的核心问题。从无人机作战效能基本问题出发,首先,剖析无人机自主性与自主能力以及自主作战能力与自主作战效能的基本概念,给出无人机自主作战效能评估的基本流程;其次,分别从任务视角和原理角度对无人装备作战效能评估指标体系、评估方法的研究现状进行综述,归纳总结3类指标体系构建的现状和4类效能评估方法进展;最后,梳理当前评估中存在的问题,展望无人机效能评估技术的发展趋势。相关研究可为后续无人机自主作战效能评估提供技术参考和方向指导,具有重要的军事意义。

无人机集群对抗决策算法研究综述

无人机集群博弈对抗已经成为未来战争的发展趋势,无人机对抗决策算法的选择对提升无人机集群作战能力至关重要。本文深入探讨了基于规则的、基于博弈论的和基于神经网络的三大类无人机集群博弈对抗决策算法,并对它们的优势和局限性进行了全面分析与总结。在此基础上,提出将“基于多智能体强化学习的信用分配模型”和“基于角色的多智能体强化学习模型”应用于无人机集群博弈对抗的研究思路。最后,强调了选择适当的决策算法对于提高无人机集群作战效能的重要性,并为未来无人机对抗决策的发展提出了有益的建议,为相关领域的研究和应用提供了深入见解。

卫星任务管控系统实战化检验分析与研究

随着航天系统运用由业务运行模式向作战使用模式转变,对卫星任务管控系统的产品质量提出了更高的要求。现行的检验试验基于满足产品的技术性能指标,检验条件单一、检验流程固定。为有效预防系统在使用中出现“不好用”“不实用”的现象,深入分析系统的实战化要求,构建了测试过程模型,提出了在上线前对系统进行全链路压测的测试方法。通过多角度想定系统应用场景,选取多样本数据,实现了最大限度地模拟实战环境,检验了系统的作战使用效能,为信息系统的实战化检验提供了一种有效的检验模式和方法。

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。