美国全球作战支持系统

美国的全球作战支持系统(GCSS)是 "武士C4I"的一个组成部分,它将人员、后勤、采办、医疗等支持功能集成到一个跨功能的环境中.GCSS将实现作战支持功能之间以及作战支持与指挥控制功能之间的端对端的信息互操作,为联合作战人员提供融合、实时的多维战场视图.主要介绍了全球作战支持系统的功能、系统组成及其未来作战能力.     

大机动格斗及近距空战的若干问题

较系统地综述了大机动格斗的一些基本战术和技术概念,引入了超机动性的一些度量,然后提出了使用具有刚体性质的质点飞机模型的可行性.最后提出在大机动格斗实战环境中,火力控制需要解决的若干问题.     

采用时序多属性评价方法的空战威胁排序

鉴于空战中威胁目标的多属性及动态变化特点,提出采用时序多属性评价方法来解决空战目标动态威胁排序问题。在建立空战威胁指标体系基础上,采用熵权法确定目标属性权重,并通过指数分布法确定各时刻权重,最后综合依据威胁目标多时刻的属性信息,实现了空战动态威胁排序。仿真结果说明该方法的合理性。     

未来中远距协同空战多目标攻击决策研究

根据先进战斗机的技术特点和未来中远距协同空战发展方向,提出一种基于先敌发现、先敌发射、先敌摧毁能力的空战态势分析模型。同时,采用一种将并行遗传算法与分布估计算法相结合的并行分布遗传算法并将其用于多目标攻击决策。最后利用具体算例进行仿真。结果表明空战态势分析模型能够较为准确地描述未来中远距多机协同空战,采用的算法具有较好的可行性和实时性,为中远距多机协同空战的多目标攻击决策提供了新的思路和方法。     

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.