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.     

Fitting Lanchester equations to the battles of Kursk and Ardennes

Lanchester equations and their extensions are widely used to calculate attrition in models of warfare. This paper examines how Lanchester models fit detailed daily data on the battles of Kursk and Ardennes. The data on Kursk, often called the greatest tank battle in history, was only recently made available. A new approach is used to find the optimal parameter values and gain an understanding of how well various parameter combinations explain the battles. It turns out that a variety of Lanchester models fit the data about as well. This explains why previous studies on Ardennes, using different minimization techniques and data formulations, have found disparate optimal fits. We also find that none of the basic Lanchester laws (i.e., square, linear, and logarithmic) fit the data particularly well or consistently perform better than the others. This means that it does not matter which of these laws you use, for with the right coefficients you will get about the same result. Furthermore, no constant attrition coefficient Lanchester law fits very well. The failure to find a good‐fitting Lanchester model suggests that it may be beneficial to look for new ways to model highly aggregated attrition. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

基于排列法的目标威胁评估模型

目标威胁评估是一个推理、决策的行为,结合现代防空作战的特点和指挥自动化系统的工作过程,分析了影响目标威胁评估的几个重要因素,应用多属性决策中的一种改进排列法建立了数学模型,验证了其可行性。     

舰载作战训练系统的概念设计

本文介绍了舰载作战训练系统的研究方向、功能、组成及各单元的任务和接口关系,并对关键技术进行了简要说明。     

基于模型的舰艇信息融合系统的研究

应用计算机仿真技术建立一个具有战术想定功能的舰艇作战系统仿真环境,来研究舰艇的多传感器信息融合,并给出部分仿真模型。     

基于改进ADC方法的陆基常规导弹主战系统作战效能评估模型

立足于实战对抗背景,运用改进的ADC效能评估模型,研究了陆基常规导弹主战系统的作战效能,其研究方法可为导弹武器作战系统的指标论证、工程研制和作战运用等提供相关理论依据.     

坦克分队遭遇战斗火力运用研究

根据遭遇战斗的特点,运用随机格斗、微分对策数理战术模型,研究坦克分队遭遇战斗最优火力运用策略问题.研究得出的结论符合坦克分队战术特点,为坦克分队指挥决策提供辅助作用.     

WSEIAC模型的中远程空空导弹武器系统效能分析

依据中远程空空导弹武器平台系统的战术指标,建立了中远程空空导弹武器平台系统的效能评价体系.对ADC扩展模型中的人为因素K进行了修正,在能力矩阵C中增加对载机生存能力和信息支持能力的体现.在此基础上给出了修正扩展模型下的系统效能,计算了在原始ADC模型下的系统效能,并对两种模型下的效能差异进行了比较.     

模糊推理在舰艇防空指挥系统中的应用

针对舰艇防空作战中遇到的不确定性问题,在科学分析影响目标状况因素的基础上,根据专家经验,运用模糊推理的原理和方法,重点分析了目标状况知识信息的表示方法,给出了模糊推理规则,研究设计了用于舰艇防空辅助决策的模糊专家系统。最后给出了一个应用实例。     

空战拦射攻击自主优化瞄准控制方法

为了解决空战自主拦射攻击问题,提出了一种自主优化瞄准控制方法。首先描述了自主拦射攻击需要解决的问题;然后给出了基本解决思路并推演出瞄准偏差方程;接着创建了自寻优瞄准控制模型并设计了瞄准控制过程;最后通过仿真实验进行了与比例导引控制方法的对比测试和鲁棒性测试,结果表明自主优化瞄准控制法对于解决自主拦射攻击问题非常有效。