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.     

一种装甲车辆CAN总线混合调度算法

针对当前装甲车辆综合电子系统节点消息传输不公平、紧急消息传输不及时等缺点,结合某型车辆消息紧急度和截止期,按照分层处理原则对总线协议进行了改进,提出了"分级决策动态均衡"的混合调度算法,并通过试验验证了算法的可行性和有效性。     

舰艇编队航渡中对空防御组织研究

为提高舰艇编队的对空防御能力,根据实际情况以及水面舰艇战术理论,从对空中威胁的判断、防空兵力的配置和计算以及对空抗击的组织等方面对编队航渡中的对空防御组织进行了论述。对舰艇编队防空建设及防空能力的提高有一定的现实意义。     

Consumer drone evolutions: trends,spaces, temporalities,threats

ABSTRACT

While the drone has become synonymous with the War on Terror, the asymmetric iconography of the battlefield is shifting. Commercially available off-the-shelf (COTS) drones are increasingly prevalent features of global battlefields, employed by non-state actors in both visualising such spaces, and the directing and inflicting of harm. As such usage increases, so too do concerns around their evolving adoption, adaptation, and potential portability into homeland spheres. While cognizant of the range of positive drone applications, it is asserted that drones nonetheless remain simultaneously bound to an inverse potential for exploitation. In examining drone risk, this article approaches the consumer drone through a series of sites and spaces through which it is technically and socially constructed. Reflecting upon industry innovation, community-driven experimentation, and evolving airspace – it calls for greater attention to the drone’s malleability, arguing that understandings of COTS drones must remain attentive to both drone potential and potential drone threat.     

基于部件模型的无人机系统小样本车辆目标识别方法

对地目标检测与识别是无人机系统典型任务之一,但受限于任务特殊性,往往难以获取足够的目标样本数据以实现高可靠的目标识别.为此,结合人的认知特性,提出一种基于部件模型的小样本车辆目标识别方法,可有效提高无人机感知能力.采用视觉显著性检测与物体性检测相结合的检测方法,提取目标可能区域;采用基于图论的GrabCut方法与最大类...     

Innovation for seapower: U.S. Navy strategy in an age of acceleration

ABSTRACT

A call for innovation as a means to outpace the acceleration of technological change and to bolster capabilities quickly is a central theme of contemporary U.S. Naval Strategy. The need for innovation is accepted, but the integration of readily available technologies into the Fleet is slow. Innovation unfolds according to “Navy-Time,” a cycle lasting between a long-decade and thirty-years. To accelerate the innovation process in the U.S. Navy, the article offers a Concept of Operations for Innovation that outlines how a deliberate process of analysis, weaponisation and integration can move new ideas and weapons into the Fleet in a strategically relevant time-frame.