基于阶层标识的无人机自主精准降落系统

随着微小型无人机（UAV）在航拍、测绘、环境监测、快递投送等民用领域的广泛应用,对微小型无人机的可用性和可靠性提出了更高的要求。为了使微小型无人机能够精确地完成自主降落,由于计算机视觉部署成本低、独立性强、信息丰富等特点,提出了通过识别匹配一种多层嵌套二维编码的阶层降落标识来进行相对定位的算法,并展示了与之对应的阶层标识检测及定位的无人机自主降落系统,由于编码的信息量与其他系统相比,具有高低空的高识别率、编码空间大等特点,故此系统可同时支持单个或多个停机坪的配置,且成本低廉,无需添加机载设备成本。最后对该系统进行仿真验证和实飞测试,表明所提出算法能够有效地实现无人机全自主降落。     

空中受油管尾流特性研究

空中加油能力几乎已成为现代战斗机必备的标志之一，本文是针对某机加装固定式受油装置后座舱内出现的飞行员难以忍痛受的噪声开展攻关研究的成果，研究对象为固定式受油装置的关键部件-受油管，文中就圆形和两种流线型剖面的受油管尾流特性开展了理论，试验和空测对比研究。     

集群无人机队形重构及虚拟仿真验证

队形重构是集群无人机（UAV）控制的重要问题，指无人机按照要求安全、无碰撞地从一个队形变换到另一个队形，其难点在于快速规划最优安全轨迹并控制无人机进行轨迹姿态的高精度跟踪。针对集群无人机队形重构的上述问题，首先，基于CAPT（Concurrent Assignment and Planning of Trajectories）算法，解决了多无人机的目标分配和轨迹生成的实时性问题，实现了集群无人机的最优安全路径规划；其次，提出一种有限时间多变量积分滑模连续控制算法，解决了无人机轨迹姿态的高精度跟踪问题，并通过MATLAB仿真验证了该控制算法的有效性；最后，为了更加真实直观地演示无人机三维仿真效果，建立了基于Gazebo-ROS的无人机仿真平台，实现了12架四旋翼无人机队形重构"建模-仿真-可视化"的一体化仿真演示，验证了上述路径规划算法和轨迹姿态控制算法的有效性。     

基于受油能力的加油机效能分析

受油能力逐渐成为加油机设计的必备能力。然而现有的加油机效能分析方法没有考虑受油能力因素的影响。为了分析受油能力对加油机效能的影响,首先,通过对历史上典型加油机远程部署战例的分析,研究受油能力对加油机作战效能的影响。其次,对加油机机队加受油过程进行详细分析,并在此基础上建立基于受油能力的加油机机队能力模型。通过对单机种加油机机队的加油能力模型和基于受油能力的加油机机队加油能力模型进行分析,分析加油机具有受油能力对加油机机队加油效能的重大影响,从而为加油机设计提供参考。     

构建以"两个能力"培养为核心的航修人才培养新模式

根据现代航空装备发展的需要，提出了既有较强的岗位任职能力，又有较强的装备改装发展能力的新型航修人才培养模式。     

Trajectory tracking control of a VTOL unmanned aerial vehicle using offset-free tracking MPC

Designing a stable and robust flight control system for an Unmanned Aerial Vehicle (UAV) is an arduous task. This paper addresses the trajectory tracking control problem of a Ducted Fan UAV (DFUAV) using offset-free Model Predictive Control (MPC) technique in the presence of various uncertainties and external disturbances. The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays, parametric uncertainties, and disturbances. The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed, namely the hover, transition, and cruise mode. The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component. Moreover, different uncertainties such as parametric, time delays in state and input, are introduced in translational and rotational components. From the previous work, the Linear Quadratic Tracker with Integrator (LQTI) is used for comparison to corroborate the performance of the designed controller. Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy. Finally, discussions are provided to demonstrate the effectiveness of the given methodology.     

海上多任务飞机与无人机协同反潜作战研究

海上多任务飞机将成为海军执行海上巡逻侦察和反潜作战的主力航空武器装备,海上多任务飞机与无人机联合编队进行协同反潜作战将是未来航空反潜的发展趋势和重要作战方式之一。介绍P-8A海上多任务飞机主要系统配置和作战能力,分析海上多任务飞机的作战使用以及海上多任务飞机与无人机协同作战的概念和模式,包括协同态势感知和协同攻潜作战,指出了未来海上多任务飞机与无人机协同作战涉及的主要关键技术。     

机载雷达测控站性能分析CSCD

陆基、海基测控设备在跟踪低空特别是超低空飞行目标时受视距影响大,作用距离有限,而机载测控站作为空中测量平台,不受视距影响,有很大发展空间。介绍了机载测控站的组成、功能和工作原理,分析了机载测控站相关参数关系和工作能力,提出了提高跟踪能力的方法措施;给出了系统跟踪距离计算公式,通过几种典型目标速度分析了系统的跟踪能力,并与陆基、海基测控设备的跟踪能力进行了比对。结果表明,机载测控站在跟踪低空目标时作用距离较远,在低弹道大射程目标飞行试验中具有较大的应用空间。     

小管径超声波流量计去噪方法

超声波流量计中存在各种噪声,直接影响流量测量的精度。文章首先分析了小管径超声波流量计中噪声的来源,从实时性的角度出发,提出了一种直接对测量时间差进行去噪的方法,避免了对原始超声波信号进行采样、分析带来的实时性差的问题。针对随机噪声和声学噪声引起的时间差数据中的误差,以硬阈值小波滤波作为第一级滤波、中值加均值滤波为第二级滤波进行处理。通过流量测量实验,验证了去噪方法的有效性,结果表明,去噪后流量计的瞬时测量误差小于1%。     

UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.