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.     

Visual-simulation region proposal and generative adversarial network based ground military target recognition

Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisy-representation. In this paper, a recognition method, involving a novel visual attention mechanism-based Gabor region proposal sub-network (Gabor RPN) and improved refinement generative adversarial sub-network (GAN), is proposed. Novel central–peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset (GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.     

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。     

烟幕--信息战的"坚盾"

针对信息成为重要战争资源及信息对抗贯穿始终的特点,介绍了烟幕干扰激光、红外、雷达、卫星以及复合干扰、扰乱心理等作用,阐述了点目标防护、野战防空和攻势迷盲等战术应用,展望了烟幕技术的发展趋势.     

一种武器装备通用防护油的研制

介绍了武器装备通用防护油的研制过程、配方、性能及应用情况。实验室理化性能评定和实际使用结果表明,该油具有功能多、性能好、通用性强和使用方便等特点,综合性能达到了国际同类产品的先进水平,可广泛应用于军事装备和民用机械设备的擦拭保养和防护。     

软件测试工具的问题及解决方法

讨论了当前软件测试工具的关键技术,并指出了其弱点,介绍了如何基于.NET技术对这些弱点进行相应的改善.     

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.