Millimetre wave radar signatures of drones

对小型无人驾驶飞行器(UAV)或无人机的检测，无论是对故意无人机活动的管理，还是对可能对安全、安全或隐私造成威胁的不受欢迎的无人机的检测，都是日益增长的要求。雷达是最有前途的传感器之一，通过使用雷达散射截面(RCS)和微多普勒(由螺旋桨叶片旋转产生)等独特的雷达特征，可以对无人机进行探测和分类。毫米波集团的研究表明，毫米波雷达在无人机探测、跟踪和分类方面可能会提供一些优势，因为它具有高的多普勒灵敏度和紧凑的系统尺寸。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。研究课题的范围包括：-1.利用圣安德鲁斯提供的数据，开发统计波动模型，分析毫米波频率下无人机的RCS特性。从机身和螺旋桨材料、大小、形状和方位角等方面表征无人机的RCS值。利用现有数据和使用圣安德鲁斯现有雷达获取更多数据，研究无人机在毫米波频率的RCS值的偏振相关性。开发商用无人机的电磁散射模型，以研究和表征螺旋桨产生的微多普勒信号。通过实验室可控环境实验数据的收集和分析，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。利用高分辨率距离像(HRRP)数据对无人机进行逆合成孔径雷达(ISAR)成像。研究了利用ISAR数据区分附加有效载荷和不同型号无人机的可行性。开发基于神经网络的无人机分类算法的实时应用技术。圣安德鲁斯已经有了大量的数据来创建培训数据库。候选人还将设计和建立试验性试验，以使数据库在无人机数量和周围环境方面更大、更多样化。

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.