反舰导弹与近程反导舰炮武器系统

在简述了反舰导弹的发展和水面舰艇防御问题的基础上,分析了近程反导舰炮武器系统抗击反舰导弹的必要性和有效性     

GNSS/SINS组合导航在地地导弹中的工程应用

介绍了基于Kalman滤波的GNSS/SINS组合导航技术在地地导弹中的应用,在发射点惯性坐标系中建立了便于工程应用的数学模型,针对某型号进行了位置速度仿真,并和目前在工程实际中应用的加权平均组合方法进行了比较。仿真结果表明,Kalman滤波技术可以应用于地地导弹工程实际中。     

先进无人战斗机(UCAV)系统概念

先进无人战斗机系统是指正在发展和将要发展的无人战斗机系统。无人战斗机系统主要由携带武器的无人飞机、用于人工控制无人飞机的控制站和将系统连接起来保持与战术环境相联系的数据网络组成。在未来战争中,无人战斗机系统的主要作战使命是空防压制、纵深打击和"空中占领"。在研制过程中需要解决自主飞行、信息传输与处理等关键技术。     

多架无人机协同作战智能指挥控制系统

给出一种基于Agent技术的多架无人作战飞机协同控制系统的设计方法,该系统由几类Agent构成情报决策Agent、单机自主决策Agent、协同决策Agent等.由于利用了Agent的两大优良特性--自主性和协同性,系统能够控制多架无人作战飞机,各无人机对不断变化的战场环境也具有很高的适应性.     

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.     

高校智能无人系统运用人才队伍建设研究

智能无人系统是能够通过先进的技术进行操作或管理而不需要人工干预的人工系统,具有自主性、智能性以及人机耦合性等特征。随着科技快速更迭、国家战略支持、国家安全保障等要素驱动,高校智能无人系统运用人才队伍建设的研究势在必行。本文认为,当前,高校智能无人系统运用人才队伍呈现出愈发注重交叉学科研究、理论联系实践、开展专业认同教育以及课程思政建设等特点,这需要高校在引进和培育、交叉和融合以及专业化和高素质上下功夫,稳步推进高水平创新团队建设和一流专业学科群建设,同时稳步加大复合型人才培养力度,以为高校智能无人系统研发提供人才和智力保证。