Millimetre wave radar signatures of drones

无人机的毫米波雷达特征

• 批准号: 2458724
• 金额: --
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2458724
• 关键词: Millimetre; wave; radar; signatures; drones

The detection of small unmanned aerial vehicles (UAVs), or drones, is a growing requirement both for the management of intentional drone activity and for the detection of unwanted drones which might cause a risk to security, safety or privacy. Radar is one of the most promising sensors with which drones can be detected and classified by using distinct radar signatures such as radar cross section (RCS) and micro-Doppler (produced by the rotation of the propeller blades). Research in the Millimetre Wave Group has shown that millimetre wave radar may offer some advantages in drone detection, tracking and classification in terms high Doppler sensitivity and compact system size. This PhD project aims to deepen the understanding of the millimetre wave radar signatures of drones and develop techniques which could be applied in a drone detection system. The scope of research topics includes:-1. Develop statistical fluctuation models to analyse RCS characteristics of drones at millimetre-wave frequencies using data available at St Andrews.2. Characterise RCS values of drones in terms of fuselage and propeller material, size, shape and aspect angles.3. Study polarisation dependencies of the RCS values of the drones at millimetre-wave frequencies by using the available data and acquiring more data by using radars available at St Andrews.4. Develop EM scattering models of commercial drones to study and characterise the micro-Doppler signatures produced by the propellers. Understanding of the features will be further strengthened by lab-based controlled environment experimental data collection and analysis.5. Wavelet analysis of the micro-Doppler signatures of drones. Develop wavelet signature based drone classification algorithm for real-time application.6. Inverse Synthetic Aperture Radar (ISAR) imaging of drones using high resolution range profiles (HRRP) data. Study the feasibility of distinguishing attached payloads and different models of drones using ISAR data.7. Develop techniques for Neural Network based drone classification algorithm for real-time application. Extensive data is already available at St Andrews to create a training database. The candidate will also design and set up experimental trials to make the database larger and more diverse in terms of number of drones and surrounding environment.

小型无人驾驶飞行器（uav）或无人机的检测是一个日益增长的需求，无论是为了管理故意的无人机活动，还是为了检测可能对安全、安全或隐私造成风险的不需要的无人机。雷达是最有前途的传感器之一，通过使用不同的雷达特征，如雷达横截面（RCS）和微多普勒（由螺旋桨叶片旋转产生），可以检测和分类无人机。毫米波组的研究表明，毫米波雷达在无人机探测、跟踪和分类方面具有高多普勒灵敏度和紧凑的系统尺寸等优势。该博士项目旨在加深对无人机毫米波雷达特征的理解，并开发可应用于无人机探测系统的技术。研究课题范围包括：-1。开发统计波动模型，利用圣安德鲁斯大学提供的数据分析毫米波频率下无人机的RCS特性。从机身和螺旋桨材料、尺寸、形状和向角等方面表征无人机的RCS值。利用现有数据和圣安德鲁斯雷达获取更多数据，研究毫米波频率下无人机RCS值的极化依赖关系。开发商用无人机的电磁散射模型，研究和表征螺旋桨产生的微多普勒特征。基于实验室的受控环境实验数据收集和分析将进一步加强对这些特征的理解。无人机微多普勒信号的小波分析。开发基于小波特征的无人机实时分类算法。使用高分辨率距离轮廓（HRRP）数据的无人机逆合成孔径雷达（ISAR）成像。研究利用ISAR数据区分无人机载具和不同型号的可行性。开发基于神经网络的无人机实时分类算法。圣安德鲁斯大学已经有了大量的数据来创建一个培训数据库。候选人还将设计和设置实验试验，使数据库在无人机数量和周围环境方面更大，更多样化。