无人机控制站通用软件平台设计

针对无人机控制站难以与多型无人机互联互通的问题，从通用控制软件的角度开展研究，分析了设计需求，提出了一种采用平台+插件的软件架构，并分析了软件架构、基于XML技术的帧格式信号和参数信息描述、动态报文解析和组包、软件界面定制等关键技术。在车载控制站和便携控制站的工程验证表明，所提解决方案实现了对多型无人机的控制，具有良好的通用性、扩展性，并支持用户自定义对外通信协议和人机交互功能、界面。     

无人机在输电线路巡检中的多种应用

无人机技术目前的快速发展使得我们国家输电线路自身的巡检能力得到了持续的提升,基于此本文在对无人机技术给予认识的情况下,在了解了输电线路巡检过程中的使用优势之后,还总结了无人机技术在当前我国线路巡检过程中的应用路径以及应用方法;之后使用案例分析的方式分析了无人机技术在当前输电线路巡检过程中的应用方法。     

无人机在露天矿方量计算中的应用

随着社会的不断发展,人们越来越重视露天矿的测绘工作。尤其是最近几年,无人机技术不断成熟,工程技术人员可以利用无人机进行露天矿方量计算,从而保证勘测工作的准确性与高效性。本文主要围绕无人机技术的相关概述进行分析,探讨利用无人机进行露天矿方量计算的有效途径,从而为相关技术人员提供一定的理论参考。     

无人机天线在雷电效应中的响应研究及性能检测

机载天线安装在无人机表面,容易成为雷电附着点,对无人机的飞行安全会产生严重威胁。 为开展相关设计,并验证设 计的有效性,针对 UHF 机载天线为研究对象,设计了片段式导流条和端口射频雷电抑制器的防护方案。 先后进行了初始先导 附着试验、外部部件瞬态感应试验、电性能测试和系统评估计算。 试验结果表明,天线损伤程度降低,片段式导流条对天线的电 性能影响不超过 0. 3 dB,射频雷电抑制器不影响驻波比、增益。 最终链路余量满足大于 3 dB 的要求,证明了设计的有效性。     

同频段混合信号中的无人机信号盲检测识别

无人机信号的探测识别技术是应对无人机黑飞滥用的关键技术之一。在实际信号监测环境中，经常会接收到多个信号的混合信号，它们在时域和频域上混叠且各信号分量调制样式相同。为解决在同频段混合信号中检测识别出无人机信号的问题，提出了一种通过谱特征分析判断无人机信号存在性的方法。分别采用基于二次方谱特征的无人机图传和WiFi混合信号检测识别算法以及基于频谱带宽特征的多无人机混合信号检测识别算法，通过对射频电路采集的信号进行仿真验证，实现了从同频段混合信号中检测识别出无人机信号分量。理论分析和实验测试结果证实了所提检测识别算法的有效性。     

Adaptive finite‐time formation tracking control for multiple nonholonomic UAV system with uncertainties and quantized input

An adaptive finite‐time formation tracking control approach is proposed for multiple unmanned aerial vehicle (UAV) system with quantized input signals in this paper. The UAVs are described by nonholonomic kinematic model and autopilot model with uncertainties. An enhanced hysteretic quantizer is introduced to avoid chattering, and some restrictions are released by using a new quantization decomposition method. Based on backstepping technique and finite‐time Lyapunov stability theory, the adaptive finite‐time controller is designed for the trajectory tracking of the multi‐UAV formation. The nonholonomic constraints are solved by a transverse function. A transformation is introduced to the control input signals to eliminate the quantization effect. Stability analysis proves that the tracking errors can converge to a small neighborhood of the origin within finite time and all the closed‐loop signals are semiglobally finite‐time bounded. The effectiveness of the proposed control approach is validated by simulation and experiment.     

Governance assessment of UAV implementation in Kenyan land administration system

The ‘fit-for-purpose’ (FFP) approach was developed to respond to the challenges that the creation of a sustainable land administration system (LAS) faces. FFP has proved its value in integrating technological innovations such as unmanned aerial vehicles (UAVs) in LAS. Technological innovations need to consider the governance context when being implemented. Understanding the relevance of the FFP approach and acknowledging the importance of the governance context, this research applies the ‘Fit-for-purpose governance assessment framework’ (FGAF). FGAF helps to understand the challenges that the implementation of UAVs can face in the LAS in Kenya. This governance assessment is based on 16 semi-structured in-depth interviews, three one-day workshops with local stakeholders and UAVs pilot studies to test the technology. The findings suggest that there are major and minor challenges concerning different attributes of FFP qualities. The governance context across counties is fragmented with a high degree of uncertainty and uneven capacity conditions. There are overlapping responsibilities and trust issues among the different actors. Participation of private and non-governmental actors is limited. Yet, steps have been taken toward the adoption of innovative approaches. Although financial resources are limited, the diversity of local and international stakeholders can work as a leverage point to support the implementation of UAVs in the mid and long-term.     

Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine

The operation of water supply channels is threatened by the occasionally occurred slope damages. Timely detection of their occurrence is critical for the rapid enforcement of mitigation measures. However, current practices based on routine inspection and structural heath monitoring are inefficient, laborious and tend to be biased. As an attempt to address the limitations, this paper proposes a bottom-up image detection approach for slope damages, which includes four steps, i.e. superpixel segmentation, feature handcrafting, superpixel classification based on support vector machine (SVM), and slope damage recognition. The approach employs a bottom-up strategy to infer the upper-level slope condition from the classification results of individual superpixels in the bottom level. Experiments were conducted to demonstrate the effectiveness of the approach. The handcrafted feature “LBP + HSV” was demonstrated to be effective in characterizing the image features of slope damages. An SVM model with “LBP + HSV” as input can reliably identify the slope condition in superpixels. Based on the SVM model, the bottom-up strategy achieved high recognition performance, of which the overall accuracy can be up to 91.7%. The proposed approach has potential to facilitate the early and comprehensive awareness of slope damages along the entire route of water channel by the integration with unmanned aerial vehicles.