飞越北极航行时间模型的研究

对飞越北极,缩短航线,节省航行时间问题进行了研究。采用了多种算法,对其有效性、适用性和复杂性进行分析讨论。假设"在旋转椭球体中,以通过球心和该椭球体上任意两点所构成的平面截此椭球体所得轨迹弧长作为曲面上两点间最短距离的近似值",在此基础上,通过运用数学软件计算得出结论:从北京出发,飞越北极抵达底特律的飞行时间可节省4.079 6h,从而使问题得到了进一步的验证。     

改革装甲装备动用制度势在必行

通过对装甲装备和其它武器装备动用制度的对比分析与探讨,提出了改进装甲新装备动用制度的初步设想。     

What you should know about the vehicle routing problem

In the Vehicle Routing Problem (VRP), the aim is to design a set of m minimum cost vehicle routes through n customer locations, so that each route starts and ends at a common location and some side constraints are satisfied. Common applications arise in newspaper and food delivery, and in milk collection. This article summarizes the main known results for the classical VRP in which only vehicle capacity constraints are present. The article is structured around three main headings: exact algorithms, classical heuristics, and metaheuristics. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

无伞末敏子弹大攻角非线性空气动力和力矩模型

以无伞末敏子弹为背景,分析了描述大攻角飞行弹丸横向运动和角运动的运动变量,建立了大攻角情况下弹丸所受非线性空气动力和空气动力矩的模型,推导出了复数形式的弹丸横向运动与角运动方程。数值积分计算表明,弹丸在特定非线性空气动力和力矩作用下,能够形成所需形式的扫描运动,证明所建力学模型正确合理,可以用于无伞末敏子弹动力学特性的分析与计算。     

基于四元数法的反辐射武器飞行仿真研究

在开发反辐射武器攻击目标的三维飞行仿真系统中或分析处理三维运动数据时,针对传统的运动姿态旋转方法存在计算繁琐、旋转不均匀性及平衡环锁定等局限问题,采用四元数法可以避免这些局限,并且几何意义明确,计算简单。因此,提出在飞行仿真系统中利用四元数法来实现反辐射武器的运动姿态及飞行速度矢量的旋转,在飞行仿真试验中实现了飞行轨迹变化光滑连续的合理效果,仿真结果表明四元数法简化了矢量旋转计算模型,优化了解算性能,能够很好地应用于反辐射武器飞行仿真试验中。     

小型电动垂直起降飞行器推进系统性能分析

小型电动垂直起降飞行器的悬停续航性能取决于推进系统的性能。基于电池、电机、电调的质量模型和螺旋桨的推力-功耗关系,利用电池的恒流放电模型建立了垂直起降飞行器的续航时间评估模型,分析了推进系统参数和有效载荷对飞行器续航性能的影响规律,研究结论可用于电动垂直起降飞行器的总体设计和推进系统选型。     

基于粗糙集理论的飞行轨迹识别

针对飞行轨迹人工识别对专家经验依赖性强,识别准确率不稳定的问题,提出一种基于粗糙集理论的飞行轨迹识别方法。首先,对飞行轨迹进行特征提取,获取用于轨迹类别判定的条件属性和决策属性;其次,利用模糊自组织神经网络对飞行轨迹决策表离散化,采用启发式遗传约简算法对条件属性约简,消除了冗余属性对识别结果的影响;最后,利用实际飞参数据进行轨迹识别,结果证明该方法准确有效。     

多约束强化学习最优智能滑翔制导方法

为提升复杂飞行任务下滑翔制导的自主性,提出一种基于最优制导与强化学习的多约束智能滑翔制导策略。引入三维最优制导以满足终端经纬度、高度以及速度倾角约束。提出基于侧向正弦机动的速度控制策略,研究考虑机动飞行的终端速度解析预测方法。针对速度控制中机动幅值无法离线确定的问题,研究基于强化学习的智能调参方法。该方法基于终端速度设计状态空间,以机动幅值设计动作空间,设计综合终端速度误差与滑翔制导任务的回报函数,采用Q-Learning实现机动幅值的智能调整。仿真结果表明,智能滑翔制导方法能够高精度满足终端多种约束,并能有效提升复杂任务下的自主决策能力。     

无人机飞行训练资源配置效率的AHP-Arena综合评价方法

为降低传统层次分析法(analytic hierarchy process,AHP)赋权过程主观片面性影响,提出了一种综合AHP与Arena仿真建模技术的无人机训练资源配置效率评价方法。基于飞行训练流程分析,确定了训练资源配置效率影响因素和效率评价指标,建立了层次型综合评价体系,概括了AHP-Arena综合评价步骤。利用Arena建模软件构建训练流程动态仿真系统并验证有效性,采用单变量数值仿真法、均方差决策法完成了影响因素客观赋权,通过线性加权综合法得出综合评价系数,实现资源配置效率评价与方案优选。实例应用说明了该方法的有效性,同时,该方法可推广至其他类似的训练过程中,亦可为其他多因素、多指标评价决策过程提供借鉴。     

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.