基于Leader-Follower编队的无人机协同跟踪地面目标制导律设计

对地面目标的自动跟踪是无人机在任务应用阶段需要解决的重要问题之一,多无人机协同跟踪能够提高对目标运动状态的估计精度并降低目标丢失的概率,因而具有重要研究意义。本文提出了一种基于Leader-Follower编队的无人机协同跟踪制导方法,解决了传统Standoff跟踪模式对地面目标的速度范围限制问题。首先,通过控制无人机的航向不断趋近于地面目标牵连跟踪圆切线方向的方法设计了Leader无人机自动跟踪地面目标的制导律并完成了稳定性证明;其次,通过控制Follower无人机的速度和航向角逐渐趋近于Leader无人机速度和航向的协同跟踪策略,分别设计了Follower无人机自动跟踪Leader无人机的制导律和编队相位协同制导律并完成了稳定性证明;最后,分别针对静止目标、匀速直线运动目标和变速运动目标的跟踪问题进行了仿真验证,结果表明所提出的制导方法能够实现对不同运动状态地面目标的自动协同跟踪,并且跟踪性能优于基于李雅普诺夫向量法的制导方法。     

无人机动态校准系统研究

针对无人机使用维护特点和计量保障需求,研究提出了无人机动态校准系统的设计方案,该系统包括电磁环境模拟、无线电信号测量和校准数据处理等3个分系统。利用该动态校准系统,可以实现无人机全系统校准,精准掌控无人机系统级技术状态,确保无人机执行任务能力保持。     

无人直升机雷达目标特性研究CSCD

雷达散射目标特性(RCS)是无人直升机的重要性能指标。以某型直升机几何参数为例,分析了无人直升机RCS的特点,基于CAD软件法和电磁计算网格法建立其几何模型并生成了机身旋翼电磁计算网格。在编制计算分析流程基础上,分析计算了无人直升机RCS性能指标,得出了无人直升机的RCS分析结果。     

无人机动态校准系统研究

针对无人机使用维护特点和计量保障需求,研究提出了无人机动态校准系统的设计方案,该系统包括电磁环境模拟、无线电信号测量和校准数据处理等3个分系统。利用该动态校准系统,可以实现无人机全系统校准,精准掌控无人机系统级技术状态,确保无人机执行任务能力保持。     

无人机飞行控制半物理仿真系统设计

飞控计算机应用到无人机上之前,需要对其进行较为全面的仿真测试。本文设计了对飞控计算机进行全面测试的飞行控制半物理仿真系统。选用RTLinux作为操作系统,使用MATLAB Simulink建立某型无人机飞控系统仿真模型,采用RTW（Real Time Workshop）工具,自动生成优化的嵌入式实时仿真代码,在线调整模型参数并监视仿真数据。大量仿真试验表明：该方法能够较大程度地提高仿真代码的效率和可靠性,降低仿真软件的开发工作量,缩短开发周期,提高仿真软件的质量和仿真系统的性能,是一种值得推广的方法。     

Multi-block SSD based on small object detection for UAV railway scene surveillance

A method of multi-block Single Shot MultiBox Detector (SSD) based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance. To address the limitation of small object detection, a multi-block SSD mechanism, which consists of three steps, is designed. First, the original input images are segmented into several overlapped patches. Second, each patch is separately fed into an SSD to detect the objects. Third, the patches are merged together through two stages. In the first stage, the truncated object of the sub-layer detection result is spliced. In the second stage, a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers. The boxes that are not detected in the main-layer are retained. In addition, no sufficient labeled training samples of railway circumstance are available, thereby hindering the deployment of SSD. A two-stage training strategy leveraging to transfer learning is adopted to solve this issue. The deep learning model is preliminarily trained using labeled data of numerous auxiliaries, and then it is refined using only a few samples of railway scene. A railway spot in China, which is easily damaged by landslides, is investigated as a case study. Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6% and obtains an improvement of up to 9.2% compared with the traditional SSD.     

Unmanned aerial vehicle swarm mission reliability modeling and evaluation method oriented to systematic and networked mission

With the development of Unmanned Aerial Vehicle (UAV) system autonomy, network communication technology and group intelligence theory, mission execution in the form of a UAV swarm will be an important realization of future applications. Traditional single-UAV mission reliability modeling methods have been unable to meet the requirements of UAV swarm mission reliability modeling. Therefore, the UAV swarm mission reliability modeling and evaluation method is proposed. First, aimed at the interdependence among the multiple layers, a multi-layer network model of a UAV swarm is established. At the same time, based on the system having the following characteristics—using a mission chain to complete the mission and applying the connectivity of the mission network—the mission network model of a UAV swarm is established. Second, vulnerability and connectivity are selected as two indicators to reflect the reliability of the mission, and aimed at random attack and deliberate attack, vulnerability and connectivity evaluation methods are proposed. Finally, the validity and accuracy of the constructed model are verified through simulations, and the model and selected indicators can meet the reliability requirements of the UAV swarm mission. In this way, this study provides quantitative reference for UAV-swarm-related decision-making work and supports the development of UAV-swarm-related work.     

基于认知云的无人机群组信息对抗系统

基于认知云的无人机群组信息对抗系统由长航时、低成本的电子战无人机群组和云计算网络构成。侦察无人机负责有限范围内的信息侦收，云计算网络融合无人机群感知结果，生成广域信息态势，动态制定系统层对抗策略，调度攻击无人机实施电磁频谱压制或信息接入攻击，同时评估对抗效能，实现信息攻防闭环。无人机群组抵近工作，可显著降低载荷设计复杂度，实现小型化、低功耗与有效性的统一设计。     

无人直升机数据采集与处理系统的设计与实现

设计以FPGA处理器为核心的基于PC I总线的无人直升机数据采集与处理系统。在设计中提出了一种基于PC I总线的多路数据采集与处理系统的总体方案。该系统可对无人直升机实验室中模拟信号、脉冲信号、开关信号进行采集和对桨距的控制脉冲信号进行输出,能够对各类信号分别进行处理与分析,实验测试表明,系统能够完成无人直升机实验室数据采集与处理的任务。