海军要地防空作战指挥中飞机有效部署区域研究

在遂行海军要地防空任务时,飞机部署区域选择对防空作战有至关重要的影响。通过对防空作战指挥中飞机防空作战指挥流程分析研究,采用战术计算方法得出了防空作战中飞机有效部署区域应满足的必要条件,根据必要条件构建了线性规划模型,并通过图解法求得有效部署区域。最后对研究成果应用领域进行了分析。     

群决策理论在多机协同空战机动决策中的应用

针对近距空战中多架战机对空中的多个敌对目标进行协同攻击的机动决策问题进行了研究。将群决策理论引入多机协同空战机动决策,首先确定了决策成员与候选方案,然后基于战场态势评估提出了采用风险决策准则的偏好排序确定方法,给出了集结偏好的序数型群决策方法。在不同空战想定条件下进行仿真,结果表明,该方法合理、可行,具有良好的应用前景。     

火力作战目标决策分析

为满足火力作战多样化的任务需求,结合对火力作战行动有效作用空间的具体解析,对其目标决策进行了深入的分析,在把握时代特征的同时,较为系统地总结出火力作战目标决策的新要求、新方法和新流程,为信息化条件下组织筹划火力作战提供建设性理论参考。     

Two novel critical shock models based on Markov renewal processes

In this paper, we investigate systems subject to random shocks that are classified into critical and noncritical categories, and develop two novel critical shock models. Classical extreme shock models and run shock models are special cases of our developed models. The system fails when the total number of critical shocks reaches a predetermined threshold, or when the system stays in an environment that induces critical shocks for a preset threshold time, corresponding to failure mechanisms of the developed two critical shock models respectively. Markov renewal processes are employed to capture the magnitude and interarrival time dependency of environment-induced shocks. Explicit formulas for systems under the two critical shock models are derived, including the reliability function, the mean time to failure and so on. Furthermore, the two critical shock models are extended to the random threshold case and the integrated case where formulas of the reliability indexes of the systems are provided. Finally, a case study of a lithium-ion battery system is conducted to illustrate the proposed models and the obtained results.     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.     

分布式指挥控制决策冲突问题的一致性模型

基于复合标度的二元语义评价方法,提出了一种用于分布式指挥控制决策冲突问题的一致性模型。该模型首先对决策者的一致程度进行判断;其次,测量出个人与整体的偏好偏差,并由模型中的反馈机制提出相关反馈建议;最后通过算例表明该模型可以较好解决决策过程中由于个人认知不同而产生的冲突问题。     

网络中心式空战仿真研究

将网络中心战思想引入空战仿真研究,提出基于复杂适应系统理论的网络中心式空战模型.对网络中心式空战子模型进行设计,使空战单元根据对局部环境的感知所获取的态势信息来调整自身个性,调整规则依据约束变量的设定和决策元规则.通过仿真实验着重考察了具有自适应性的空战单元在空战对抗中的作战表现,实验结果初步验证了其战斗效能优势.     

（S，S）策略随机存储系统库存极限分布研究

首先给出了（s,s）策略随机存储系统极限库存分布的概念及更新过程的一些基本知识。利用更新过程的理论给出了极限库存分布的解析表达,并讨论了需求分布为指数分布的情形。然后利用系统仿真的方法进行验证,表明解析方法与仿真方法的结果完全一致。论文结果说明了解析推导的正确性和系统仿真方法的科学性。     

Optimization of steerable sensor network for threat detection

A Markov chain approach to detecting a threat in a given surveillance zone by a network of steerable sensors is presented. The network has a finite number of predetermined states, and transition from one state to another follows a Markov chain. Under the assumption that the threat avoids detection, two game theoretic problems for finding an optimal Markov chain (two surveillance strategies) are formulated: the first maximizes the probability of threat detection for two consecutive detection periods, whereas the second minimizes the average time of detection for the worst‐case threat's trajectory. Both problems are reduced to linear programming, and special techniques are suggested to solve them. For a dynamic environment with moving noise sources, the optimal Markov chain changes at each detection period, and the rate of convergence of the Markov chain to its stationary distribution is analyzed. Both surveillance strategies are tested in numerical experiments and compared one with another. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

基于运动相似性的监控轨迹聚合分析

对于监控轨迹序列集,提出一种使用运动特征发现其潜在语义内容的方法.在特征统计估计阶段,将轨迹曲线中的拐点特征加入到核密度估计过程中,得到准确平滑的方向多性态分布;使用隐马尔科夫模型估计轨迹子类中存在的串行或并行空间模式.最后以运动特征分布为基础,提出一种基于运动相似性的轨迹层次聚合模型.实验结果表明,该模型可以有效地分...