某于动态博弈的目标分配方法研究

以博弈论为基础,防空火力单元与攻击飞机多次对抗为作战背景,建立了基于动态博弈的防空火力单元目标分配模型.利用双矩阵博弈纳什均衡求解与数学规划的关系,建立了求解纳什均衡的二次规划,利用混合粒子群算法进行求解.仿真结果表明:用博弈论研究防空火力单元与攻击飞机多次对抗过程中的目标分配问题时,符合实际作战过程,真实性好,应用价...     

基于对局迭代的无人机空战博弈研究

基于对局迭代对多无人机协同空战博弈决策问题进行了研究.首先,根据敌我双方作战态势和效能参数信息,利用矩阵对策法建立敌我双方对抗支付博弈模型,得到支付矩阵;然后,根据混合策略纳什均衡的定义及其推导过程给出了使用对局迭代求解空战博弈混合策略纳什均衡的方法与求解步骤;最后,通过仿真实例验证了该方法的可行性及有效性,为解决多无...     

基于排队论的多通道防空武器系统作战效能研究

对于由多个同类型的单通道火力单元组成的防空武器系统,运用排队理论建立了防空武器系统作战效能分析模型.将对空作战过程视为具有两个服务阶段的先到先服务的排队系统,并将空袭目标持续入侵视为泊松到达.在作战过程中,只有空闲的火力通道才能对入侵的空袭目标进行服务,一旦目标在通过防空区的过程中未被服务或未被击中则突防成功.基于作战想定和计算分析,研究了防空武器系统的作战效能,为辅助作战指挥决策,优化火力单元配置等提供了一定的参考依据.     

混编式防空导弹群目标分配模型

为达到高技术条件下防空作战的目的,混编式防空导弹集群作战成为符合部队实际的作战模式.混编式防空导弹群的目标分配问题属于非线性规划问题,如用一般的方法求解,过程将十分复杂,满足不了防空作战实时性要求.本文将动态规划的思想应用于目标分配,在一定的假设前提下,设计了一种基于多元动态规划的混编式防空导弹目标分配模型,并结合具体的实例计算验证了模型的正确性.结论表明,所建立的模型简单、实用,对进一步探讨符合实战要求的目标分配模型提供了参考.     

空袭火力资源分配综述

针对常用的3类火力分配算法存在的不足,对其进行了研究和改进,以解决信息化条件下空袭火力资源分配的建模问题.分析了火力资源分配的对抗性,以及信息化条件下空袭火力分配对抗系统,总结了火力分配的研究现状,并分别分析了3类不同的火力分配算法的研究情况,指出混合优化分配算法在处理火力分配问题的优势.同时分析了3类不同的火力分配算法在处理信息化条件下的空袭火力资源分配时存在的不足,提出使用博弈论方法建立火力分配模型的可行性和有效性,并提出利用混合优化算法求解均衡解的新思路.该研究为建立接近于实战的空袭火力资源分配模型提供了新的方向.     

舰空导弹对空火力分配研究

威胁估计和火力分配是现代防空作战过程中需要解决的2个重要问题。对来袭目标威胁等级的判定可以辅助指挥员迅速做出决策。火力分配是对火力单元寻求最优分配方案,以达到毁伤敌目标的目的。基于威胁目标的各项影响因素,建立相应的威胁评估模型,求得来袭目标威胁度,并根据评估结果,利用匈牙利算法优化火力分配。     

基于火力分配环的舰艇编队防空作战体系结构研究

舰艇编队防空涉及到众多作战单元的协调指挥,火力分配,良好的C4ISR体系结构至关重要。针对舰艇编队防空的作战过程进行体系结构建模,结合火力分配环的概念,应用美军《国防部体系结构》作战视图对对战过程进行建模分析。给出了作战体系结构UML视图产品,描述了编队防空C4ISR系统的作战需求。     

多批编队目标的实时火力分配决策

在对现代防空作战分析的基础上,提出了针对多批编队目标的实时火力分配决策方法。首先利用马尔可夫理论对编队目标进行动态决策,得到需分配的目标类型,再根据当前态势和火力单元特性对需分配的目标进行静态实时火力分配,系统不断循环直至目标分配完毕,最后用实例进行了分析,计算结果表明了该方法的有效性。     

基于遗传的人工鱼群优化之武装直升机对地攻击火力分配决策

结合遗传算法和人工鱼群算法的优点对武装直升机对地攻击作战的火力分配问题进行研究,建立了火力分配的教学模型,并利用基于遗传算法的人工鱼群优化算法实现武装直升机对地攻击的火力分配.仿真实验结果表明,基于遗传算法的人工鱼群优化算法解决火力分配问题不仅收敛速度快、效果好,而且运行速度快、求解精度高,满足火力分配实时性和准确性的...     

防空雷达对抗ARM生存能力分析

防空雷达首先要解决好生存问题,然后才能谈及作战能力。文中首先建立了ARM攻击过程的实体流程图模型,分析了单个火力单元中防空雷达的生存能力及射击ARM载机的时机。在此基础上,进一步探讨了防空雷达组网情况下的生存能力分析。     

基于博弈论的无线传感网络节点攻防优化

针对无线传感器网络各节点在安全需求与资源消耗上存在的矛盾,提出一种基于博弈论的无线传感网络节点优化博弈模型.首先,通过分析网络节点中攻击方的攻击代价与防守方的防守开销,基于博弈论分析攻防双方的效用函数并构造攻防博弈模型;其次,根据网络节点中攻防双方选择的不同行动策略,结合信息论技术将攻防双方抽象成随机变量,并设计博弈信...     

基于博弈的LTE负载均衡算法研究

针对LTE网络中小区之间负载不均衡的问题,提出了一种结合经济学中的博弈理论的解决方案.通过将博弈理论中的拍卖模型引入到LTE系统模型中,建立竞拍双方的效用函数,求出均衡解最大化其效用函数,从而合理地调整切换参数将热点小区的负载转移到目标小区.仿真结果证明了该算法的有效性,该方案可以合理有效地解决小区间负载不均衡问题、优化网络资源和提高资源利用率.     

Optimal strategy selection method for moving target defense based on signaling game

To solve the problem of the optimal strategy selection for moving target defense,the defense strategy was defined formally,the defense principle from the perspective of attack surface shifting and exploration surface enlarging was taken into account.Then,network attack-defense behaviors were analyzed from the sight of dynamic confrontation and bounded information.According to the analysis of attack-defense game types and confrontation process,the moving target defense model based on signaling game was constructed.Meanwhile,the method to quantify strategies was improved and the solution of perfect Bayesian equilibrium was proposed.Furthermore,the optimal defense strategy selection algorithm was designed by the equilibrium analysis.Finally,the simulation demonstrates the effectiveness and feasibility of the proposed optimal strategy and selection method.     

博弈条件下雷达波形设计策略研究

为提高电子战中弹载雷达检测性能,该文提出基于纳什均衡的雷达波形设计方法。首先建立电子战条件下雷达与干扰信号博弈模型,基于最大化信干噪比(SINR)准则,分别设计了雷达和干扰的波形策略;然后通过数学推导论证了博弈纳什均衡解的存在性,设计了一种重复剔除严格劣势的多次迭代注水方法来实现纳什均衡;通过二步注水法推导了非均衡的maxmin优化方案;最后通过仿真实验测试不同策略下雷达检测性能。仿真结果证明,基于纳什均衡的雷达信号设计有助于提升博弈条件下雷达检测性能,对比未博弈时,雷达检测概率最高可提升12.02%,较maxmin策略最高可提升3.82%,证明所设计的纳什均衡策略更接近帕累托最优。     

Research on active defense based on multi-stage cyber deception game

In view of the characteristic that attacker depended on the detected information to decide the next actions,the non-cooperative signal game theory was applied to analyze cyber attack and defense.The signal deception mechanism in the process of cyber attack and defense was considered deeply by constructing a multi-stage cyber deception game model,and the dynamic analysis and deduction of the multi-stage cyber attack and defense was realized by considering the attenuation of cyber deception signals.A solution for multi-stage cyber deception game equilibrium was improved based on analysis of cyber attack and defense,and an optimal algorithm for selecting cyber deception defense strategies was designed.The effectiveness of the model is verified by simulations.The rules of multi-stage cyber deception games are summarized based on the results,which can provide effective guidance for the research on cyber active defense.     

Defense decision-making method based on incomplete information stochastic game and Q-learning

Most of the existing stochastic games are based on the assumption of complete information,which are not consistent with the fact of network attack and defense.Aiming at this problem,the uncertainty of the attacker’s revenue was transformed to the uncertainty of the attacker type,and then a stochastic game model with incomplete information was constructed.The probability of network state transition is difficult to determine,which makes it impossible to determine the parameter needed to solve the equilibrium.Aiming at this problem,the Q-learning was introduced into stochastic game,which allowed defender to get the relevant parameter by learning in network attack and defense and to solve Bayesian Nash equilibrium.Based on the above,a defense decision algorithm that could learn online was designed.The simulation experiment proves the effectiveness of the proposed method.     

Optimality and Complexity of Pure Nash Equilibria in the Coverage Game

In this paper, we investigate the coverage problem in wireless sensor networks using a game theory method.We assume that nodes are randomly scattered in a sensor field and the goal is to partition these nodes into K sets. At any given time, nodes belonging to only one of these sets actively sense the field. A key challenge is to achieve this partition in a distributed manner with purely local information and yet provide near optimal coverage. We appropriately formulate this coverage problem as a coverage game and prove that the optimal solution is a pure Nash equilibrium. Then, we design synchronous and asynchronous algorithms, which converge to pure Nash equilibria. Moreover, we analyze the optimality and complexity of pure Nash equilibria in the coverage game. We prove that, the ratio between the optimal coverage and the worst case Nash equilibrium coverage, is upper bounded by 2 ? 1 m+1 (m is the maximum number of nodes, which cover any point, in the Nash equilibrium solution s*). We prove that finding pure Nash equilibria in the general coverage game is PLS-complete, i.e. ?as hard as that of finding a local optimum in any local search problem with efficient computable neighbors?. Finally, via extensive simulations, we show that, the Nash equilibria coverage performance is very close to the optimal coverage and the convergence speed is sublinear. Even under the noisy environment, our algorithms can still converge to the pure Nash equilibria.     

Joint random access and power control game in ad hoc networks with noncooperative users

We consider a distributed joint random access and power control scheme for interference management in wireless ad hoc networks. To derive decentralized solutions that do not require any cooperation among the users, we formulate this problem as noncooperative joint random access and power control game, in which each user minimizes its average transmission cost with a given rate constraint. Using supermodular game theory, the existence and uniqueness of Nash equilibrium are established. Furthermore, we present an asynchronous distributed algorithm to compute the solution of the game based on myopic best response updates, which converges to Nash equilibrium globally. Finally, a link admission algorithm is carried out to guarantee the reliability of the active users. Performance evaluations via simulations show that the game-theoretical based cross-layer design achieves high performance in terms of energy consumption and network stability.     

基于博弈论的无线传感器网络恶意程序传播模型

恶意程序传播是无线传感器网络（wireless sensor network,WSN）面临的一类重要安全问题。从博弈论的角度对WSN恶意程序传播的微观机理进行分析,建立了WSN的攻防博弈模型,求出了博弈模型的混合纳什均衡解,并根据博弈双方的混合纳什均衡策略确定恶意程序的传染概率,从而建立了WSN的恶意程序传播模型。通过使用元胞自动机方法对WSN的恶意程序传播过程进行模拟,揭示了恶意程序的传播速度与博弈参数之间的关系,研究结果对抑制WSN恶意程序传播具有理论指导意义。     

基于系统动力学的网络安全攻防演化博弈模型简

基于非合作演化博弈理论,提出了在攻防双方信息不对称情况下具有学习机制的攻防演化博弈模型。结合攻防效用函数,对非合作演化博弈攻防过程中的纳什均衡点的存在性和唯一性进行论证。用系统动力学建立演化博弈模型进行仿真,仿真结果表明引入第三方动态惩罚策略的演化博弈模型存在纳什均衡,指出在网络安全技术进步的同时,发展攻击者追踪技术,增强网络攻击行为可审查性,实现动态惩罚,是解决网络安全问题的重要途径。