An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years, the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper, which is based on the Hyperspectral Features (HFs) of hypersonic targets covered with plasma sheath during high-speed flight. First, a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs. Then, the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar. Finally, an unsupervised classification method based on the Density Peak Clustering (DPC) for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy. The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set. The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient.     

Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles(UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive des...     

无人机系统人因要求研究

针对无人机系统与有人机座舱人机接口的不同特点,为无人机系统在民用空域正常安全飞行,开展包含飞行、导航和通信三大基本任务的人因要求研究。通过对无人机系统3项系统级任务流程分析,综合考虑人员感知、认知、决策,以及操作需求,得出人员任务、信息显示、控制输入、人机界面,以及通用要求5个方面的人因要求。文中主要从人因工程要求研究方法的角度,给出了无人机系统人因要求的主要内容,未进行详细展开,研究结果适用于无人机系统级人因要求研究和确定。     

Computational engineering analysis of external geometrical modifications on MQ-1 unmanned combat aerial vehicle

This paper focuses on the effects of external geometrical modifications on the aerodynamic characteristics of the MQ-1 predator Unmanned Combat Aerial Vehicle(UCAV) using computational fluid dynamics. The investigations are performed for 16 flight conditions at an altitude of7.6 km and at a constant speed of 56.32 m/s. Two models are analysed, namely the baseline model and the model with external geometrical modifications installed on it. Both the models are investigated for various angles of at...     

城市风场环境中的无人机快速航迹规划方法

密集的城市障碍环境以及复杂的城市风场干扰对航迹规划的实时性和航迹跟踪的准确性提出了严格要求,为此提出一种城市风场环境中的小型无人机(UAVs)快速航迹规划方法。首先,为了保证航迹规划的高效性,对固定翼无人机运动学方程进行了合理简化。其次,由于障碍环境中的最优航迹难以直接完全塑造,因此根据状态受限的最优控制理论给出了可以使用螺旋线与直线构建近似最优航迹的结论,并据此提出了一种针对城市环境的三维航迹规划方法。然后,通过对无人机运动学模型的分析,从规划角度提出了风场干扰下的航迹设计准则。仿真实验中,首先通过算法对比实验,验证了航迹规划方法的高效性;然后使用六自由度(DOF)飞机模型分别在无风场干扰和有风场干扰的环境下进行了航迹跟踪实验,实验结果证明了风场干扰下航迹设计准则的有效性。     

小型固定翼无人机集群综述和未来发展综述

围绕小型固定翼无人机集群这一难度高、发展快、应用前景广阔、多学科交叉的新方向,从集群系统内涵、现有典型项目、关键技术3个角度综述了国内外小型固定翼无人机集群的研究现状。在系统梳理集群系统内涵和应用优势的基础上,从集群协同模式探索、分布指挥体系构建、核心关键技术突破和集群验证等4个视角总结现有典型项目,从体系架构、通信组网、决策与规划、飞机平台、集群飞行、集群安全与集群指控等7个核心点综述了技术研究现状。最后,综合小型固定翼无人机集群中亟需解决的关键技术,展望了这一领域未来的发展趋势。     

面向持续战域感知的近空间飞行器技术

未来战争呈全空域立体化发展趋势,近空间的深入探索和有效利用日益受到重视.本文从热力学特性和电离特性角度,概述了近空间大气环境.重点以持续的战域感知为军事应用背景,介绍了现有的C4ISR能力,进而归纳评述了相关近空间飞行器的技术发展、工程设计、概念应用.分析指出,近空间无人HALE飞机具有填补持续的C4ISR能力中所有空缺的潜力.     

太阳能飞机循环飞行的高度剖面能量策略

为满足设定的太阳能飞机多日连续飞行条件,依据飞行过程中当前时刻的飞行高度、光伏输出功率、动力电池组余量等系统状态参数,研究如何分配动力电池组充放电和电推进系统输入等功率。所用策略立足于实时功率平衡,充分利用正午前后的光伏峰值功率用于飞机爬升及充电,在午后下滑过程中利用全部光伏输出,以最大化利用光伏资源;在光伏有效输出不足时则以一定的维持功率下滑,使能量的综合损失最小。方法能够提高以预定夜间飞行高度连续多日续航的成功率,提升飞行高度、纬度、季节范围或搭载能力,或者拓展这几种飞行条件的组合域,优化太阳能飞机的适用性。     

无人机在恶劣气象条件下的自主决策技术

无人机(UAV)在实际战场环境中受恶劣气象条件等许多不确定因素的影响,因此必须不断提高其自主决策能力。主要研究无人机自主探测恶劣气象并进行自主决策的问题,首先介绍了影响无人机飞行的典型恶劣气象条件,然后提出了利用机载传感器获取气象信息并建立恶劣气象数学模型的方案,并提出了采用专家系统理论解决无人机遭遇恶劣气象时的自主决策问题。最后,对无人机在风切变、雷暴和紊流3种常见恶劣气象下的自主决策过程进行仿真。经分析,决策结果合理,仿真结果验证了该方法的可行性和有效性。     

无人机近距离编队飞行模型建立及控制器设计

针对无人机编队飞行问题，提出了无人机近距离编队飞行的建模方法。采用全状态量反馈．同时将气动干扰作为非线性反馈，设计出一套适用于无人机近距离编队飞行的自动驾驶仪。通过仿真验证，表明此控制器能较好地稳定优化后的编队飞行结构，并具有良好的鲁棒性。