多级影响图在无人机群协同空战机动决策中的应用

现代空战多为机群对机群的空战。针对无人机群空战中协同攻防机动决策的问题,首先提出了针对单机对单机的在不确定环境下基于多级影响图的空战决策建模。在此基础上对无人机群进行分组,并根据无人机的空战能力划分角色,在空战初始确定小组内的长机与僚机。通过引入协同因子,对空战机动决策中的当前局势概率重新进行加权修正,很好地解决了单元内各成员协同配合的问题,从而将单纯的一对一空战引申为多机对多机协同空战。仿真结果表明所建立的决策模型能够有效地解决无人机群协同空战的机动决策问题。     

单步预测影响图法在空战机动决策中的应用

提出了一种基于单步预测的多级影响图法,并将该理论应用在单机连续空战机动决策系统建模中.在决策系统的建模过程中考虑了影响无人机机动决策的各要素,包括无人机动力学方程、决策者的偏好以及战斗双方态势的不确定性等.机动决策的态势基准是通过无人机动力学方程迭代获得,运动方程中的控制量通过求解单步预测影响图获得.该预测影响图算法,效能函数的起点不断向后平移,建立了离散空间空战决策的纳什平衡,通过求解效能函数的最大值就能获得每一步的最优控制解.最后仿真验证该方法有效性,元人机模型采用质点运动方程,使用VC++实现并机动决策算法.分析表明提供了一种新型无人机机动决策算法,发展了无人机自主空战机动决策系统.     

无人作战飞机空战自主机动决策研究

针对无人作战飞机自主空战的要求,通过空战机动方式的分析,提出一种基于风险型决策的空战自主机动决策算法。以对策论为基础,构建空战机动攻击阶段的对策模型,通过模糊决策分析找出最优机动策略。就不同条件下的实例进行仿真,仿真结果表明,该算法考虑空战机动决策的主动性和风险性,体现空战过程的对抗性,符合空战实际情况。     

基于多属性决策和态势估计结果的空战威胁评估方法

结合空战的特点,对影响目标威胁评估的属性进行了分析,并用层次分析法确定各属性的权值.根据多属性决策理论和方法,给出目标的威胁度,再根据空战的态势估计结果修正威胁度值,最终得到威胁排序结果,为传感器管理和火力分配提供依据.     

基于风险型决策的多目标空战机动策略研究

通过对空战机动策略的分析,提出了一种基于风险型决策的多目标机动决策方法.采用对策论和空战态势评估的相关方法,将空战态势作为选择机动策略的依据.考虑实际的空战情况,在满足机动决策能力要求的前提下,改进了预测时间的选择方法.采用与态势有关的预测时间函数,使计算速度能够满足空战快速性的要求.在Matlab环境下对实例进行了仿真.结果表明,该方法考虑了选择机动的风险性,提高了计算速度,更符合空战实际情况.     

基于神经网络的自动机动决策设计

空战智能决策技术是目前广泛被研究和使用的一种新技术。本文针对使用人工神经网络制定空战机动决策的方法进行了一些初步研究,并对仿真结果进行了对比分析。     

决策影响图方法在三维空战决策中的应用

针对以往空战建模通常仅考虑单方决策的不足,建立了考虑交战双方对抗决策的三维双机空战决策模型,应用影响图分析法描述了空战中飞行员的决策过程,并进行了三维立体格斗空战的仿真研究。仿真结果表明应用决策影响图的三维空战决策模型能够逼真地模拟空战格斗过程;该空战决策方法在不确定情况下的决策效果理想,攻防兼顾,可以适用于各种空战环境。     

基于人工势场启发粒子群算法的空战机动决策

以敌我双机空战为背景,基于滚动时域控制思想对空战机动决策进行研究。借鉴人工势场的思想构建了空战人工势场,重点分析了人工势场函数的构建,变权重函数的建立,提出了人工势场启发粒子群算法的空战机动决策方法,最后进行仿真验证。仿真结果表明,该方法能有效消除人工势场的局部极小值问题,同时也改善了粒子群算法全局搜索能力,避免了早熟,得到的结果是有效的。     

目标飞机自主空战战术机动仿真

建立了目标飞机飞行运动模型和基本飞行机动控制方法,根据现代空战特点、空中态势、威胁环境和机载武器性能等因素,设计了目标飞机进攻、防御战术机动动作,并采用机动动作链实现了战术机动动作的执行。同时建立了目标自主空战决策专家系统,实现了特定的空战态势和武器挂载条件下自主空战仿真,为模拟对抗训练提供了逼真的虚拟对手,也为态势估计、信息融合等研究工作提供了符合实际的目标信息。     

基于Q-Learning算法的无人机空战机动决策研究

针对无人机空战对抗自主机动决策问题,设计了侧向机动决策算法。通过加入启发式因子的方式和双Q表交替学习的机制,弥补了传统Q-Learning算法学习速度慢、无效学习多的不足。通过路径规划仿真和数据的对比,验证了改进Q-Learning算法具有更好的稳定性和求解能力。设计了动态的栅格规划环境,能够使无人机根据变化的空战态势自适应调整栅格尺寸大小,且对求解的速率不产生影响。基于Q-Learning算法,构建了无人机空战对抗侧向机动决策模型,并通过武器平台调换的方式验证了改进Q-Learning算法能显著提升无人机空战胜负比。     

一种近似动态规划的无人机机动决策方法

针对空战机动决策时出现的“维数爆炸”问题,该文提出一种基于近似动态规划的群智能空战机动决策方法。首先建立无人机空气动力学模型和空战态势优势指标函数。其次,利用近似动态规划的思想,将空战过程按时间域划分为多个规划时域,在每个规划时域内,提出人工势场引导下的改进蚁狮优化算法快速逼近最优控制量,有效裁减搜索空间。通过与专家系统法进行仿真对比,表明所提方法解决高动态、实时性强的无人机机动决策问题的有效性和可行性。     

高超声速滑翔飞行器机动状态识别方法研究

高超声速滑翔飞行器(HGV)的迅猛发展改变了传统的作战样式,开辟了军事斗争的新领域。对HGV的机动状态进行识别可以为威胁评估、轨迹预测和防御决策提供有力支撑。为提高HGV机动状态识别精度,该文提出一种基于注意力机制的卷积长短时记忆网络识别模型(AT-ConvLSTM)。在对HGV进行机动建模和特性分析基础上,将HGV在空间的机动状态分为8类,构造了对应的特征识别参数,建立了包含不同初始条件和控制模式下HGV机动轨迹的轨迹库。推导了从雷达跟踪信息到特征识别参数的转换步骤,使用提出的状态识别模型对HGV机动轨迹的时空特征进行提取,并通过SoftMax分类器输出机动状态分类。最后,通过仿真实验对模型性能进行验证。结果表明,所提状态识别模型能够有效在线识别HGV机动状态,具有较好的实时性和准确性。     

通信系统抗干扰能力对作战指挥效率的影响评估研究

从复杂电磁环境下陆军合成作战的实际需要出发,运用概率论和作战指挥运筹理论,建立了通信系统抗干扰能力和作战指挥效率的评估模型,根据作战中可能使用的作战指挥方式,进行了通信系统抗干扰能力和作战指挥效率相关性的数学分析,结合实例说明了所建模型的具体应用,得出了基于量化的可供作战指挥决策的有关结论。     

Three-class ROC analysis--the equal error utility assumption and the optimality of three-class ROC surface using the ideal observer.

Previously, we have developed a decision model for three-class receiver operating characteristic (ROC) analysis based on decision theory. The proposed decision model maximizes the expected decision utility under the assumption that incorrect decisions have equal utilities under the same hypothesis (equal error utility assumption). This assumption reduced the dimensionality of the "general" three-class ROC analysis and provided a practical figure-of-merit to evaluate the three-class task performance. However, it also limits the generality of the resulting model because the equal error utility assumption will not apply for all clinical three-class decision tasks. The goal of this study was to investigate the optimality of the proposed three-class decision model with respect to several other decision criteria. In particular, besides the maximum expected utility (MEU) criterion used in the previous study, we investigated the maximum-correctness (MC) (or minimum-error), maximum likelihood (ML), and Nyman-Pearson (N-P) criteria. We found that by making assumptions for both MEU and N-P criteria, all decision criteria lead to the previously-proposed three-class decision model. As a result, this model maximizes the expected utility under the equal error utility assumption, maximizes the probability of making correct decisions, satisfies the N-P criterion in the sense that it maximizes the sensitivity of one class given the sensitivities of the other two classes, and the resulting ROC surface contains the maximum likelihood decision operating point. While the proposed three-class ROC analysis model is not optimal in the general sense due to the use of the equal error utility assumption, the range of criteria for which it is optimal increases its applicability for evaluating and comparing a range of diagnostic systems.     

Three-Class ROC Analysis—The Equal Error Utility Assumption and the Optimality of Three-Class ROC Surface Using the Ideal Observer

Previously, we have developed a decision model for three-class receiver operating characteristic (ROC) analysis based on decision theory. The proposed decision model maximizes the expected decision utility under the assumption that incorrect decisions have equal utilities under the same hypothesis (equal error utility assumption). This assumption reduced the dimensionality of the “general” three-class ROC analysis and provided a practical figure-of-merit to evaluate the three-class task performance. However, it also limits the generality of the resulting model because the equal error utility assumption will not apply for all clinical three-class decision tasks. The goal of this study was to investigate the optimality of the proposed three-class decision model with respect to several other decision criteria. In particular, besides the maximum expected utility (MEU) criterion used in the previous study, we investigated the maximum-correctness (MC) (or minimum-error), maximum likelihood (ML), and Nyman–Pearson (N-P) criteria. We found that by making assumptions for both MEU and N–P criteria, all decision criteria lead to the previously-proposed three-class decision model. As a result, this model maximizes the expected utility under the equal error utility assumption, maximizes the probability of making correct decisions, satisfies the N–P criterion in the sense that it maximizes the sensitivity of one class given the sensitivities of the other two classes, and the resulting ROC surface contains the maximum likelihood decision operating point. While the proposed three-class ROC analysis model is not optimal in the general sense due to the use of the equal error utility assumption, the range of criteria for which it is optimal increases its applicability for evaluating and comparing a range of diagnostic systems.