Advanced High Resolution Methods for Radar Imaging and Micro-Doppler Signature Extraction

成像雷达是机载或星载雷达，它通过发射和接收电磁能量来生成被照射区域的反射率图。在众多类型的微波传感器中，合成孔径雷达（SAR）因其高空间分辨率和全天候作战能力而备受关注。SAR具有良好的二维分辨率能力，已经发展到满足民用和军用用户的各种应用。这些应用集中在目标成像和地形测绘上。目标是雷达照亮的特定目标。典型的目标是人造的，由多个散射中心组成。成像雷达系统必须区分位于近距离的单个和多个散射体。名义上，分辨率是图像中相邻散射体之间分离它们所需的最小距离。高分辨率提供了将复杂物体或场景成像为多个独立散射中心的能力。这种类型的图像提供了检测、表征和识别感兴趣的特定对象的详细信息。由于目标识别在军事应用中的重要性，许多发展努力都是针对提高雷达分辨率。军事SAR应用包括情报收集、战场侦察和武器制导。民用应用包括地形测绘、溢油监测、海冰表征和跟踪、农业分类和评估、土地利用监测以及行星或天体调查。通常情况下，成像雷达在被照体中提供散射体的二维表示，而在第三维中没有散射体的分辨率或定位。一般来说，我们说的是在距离和跨距离或方位方向上的雷达分辨率（发射器和接收器位于同一位置）。双基地雷达，其中发射器和接收器放置在不同的物理位置有几个操作优势。特别是这种双基地系统有助于提高接收机的生存能力，同时将接收机成本降至最低。此外，当一个或两个平台在非线性平面路径中机动时，允许在三维中计算分辨率，从而促进目标高度信息以及距离和交叉距离分辨率的获取。当雷达询问移动目标时，传统上利用目标的多普勒进行识别和表征。如果目标具有额外的旋转、振动或其他内部运动，那么这些运动就会从主多普勒分离出额外的频谱分量。它们被称为微多普勒分量，并作为主多普勒周围的附加边带存在。人类走路或跑步时，由于手臂和腿部的摆动，会出现微多普勒现象。一辆军用坦克将展示由于车轮轨迹的微多普勒，而直升机和发动机目标将展示关键的微多普勒组件。利用时频信号表示法，如短时傅里叶变换和小波分析，过去已被用于检测这些微多普勒。高质量的微多普勒信号是新型自动目标识别系统的重要组成部分。提取的质量直接关系到提取的微多普勒分辨率。本研究的目的是探索新的信号处理技术，以提高成像雷达算法和微多普勒特征提取的分辨率。这项工作将基于分数阶傅里叶变换和经验模态分解，为双基地聚光SAR图像形成和微多普勒特征提取提供新的数学关系。本文将实现FrFT计算引擎，并在模拟和实际数据上对算法进行测试。

Imaging radars are airborne or spaceborne radars which generate a reflectivity map of an illuminated area through transmission and reception of electromagnetic energy. Among many types of microwave sensors, special attention has been paid in the past to Synthetic Aperture Radar (SAR) because of its high spatial resolution, day or night all weather operational capabilities. With its fine two-dimensional resolution capability SAR has evolved to satisfy a variety of applications for both civilian and military users. These applications centre on target imaging and terrain mapping. A target is a specific object of interest that the radar illuminates. The typical target is man-made and consists of multiple scattering centres. An imaging radar system must distinguish between single and multiple scatters located in close proximity. Resolution is, nominally, the minimum distance needed between adjacent scatters to separate them in the image. Fine resolution provides the capability to image a complex object or scene as a number of separate scattering centres. This type of image provides detailed information to detect, characterize, and identify specific objects of interest. Because of the importance of object identification in military applications, much development effort has been directed at improving radar resolution. Military SAR applications include intelligence gathering, battlefield reconnaissance, and weapons guidance. Civilian applications include topographic mapping, oil spill monitoring, sea ice characterization and tracking, agricultural classification and assessment, lands use monitoring, and planetary or celestial investigations. Normally imaging radars provide a two-dimensional representation of a scatterer in the illuminated volume with no resolution or positioning of scatterer in the third dimension. Generally, we speak of monstatic (the transmitter and receiver are co-located) radar resolution in the range and cross-range or azimuth directions. Bistatic radars, where the transmitter and receiver are positioned in different physical positions have several operational advantages. In particular such bistatic systems help to increased receiver survivability while minimising receiver cost. Furthermore when one or both of the platforms are manoeuvring in an non linear planar path allows the resolution to be computed in the 3rd dimension thus facilitating the acquisition of target height information as well range and cross range resolution.When a radar interrogates a moving target it is traditional to exploit the target's Doppler for identification and characterisation. If the target possesses additional rotational, vibration or other internal motions then these induce additional spectral components separate from the main Doppler. These are termed microdoppler components and reside as additional sidebands around the main Doppler. A human walking or running will exhibit microdopplers due to swinging arms and leg movements. A military tank will exhibit microdopplers due to the wheel tracks while a helicopter and engine target will exhibit key microdoppler components. The use of time frequency signal representation such as the short time Fourier transform and wavelet analysis has been used to examine these microdopplers in the past. Good quality microdoppler signatures are important in new automatic target identification and recognition systems. Quality is directly related to the extracted microdoppler resolution extracted.The aim of this work is to explore new signal processing techniques which can be used to improve the resolution of the imaging radars algorithms and microdoppler signature extraction. The work will derive new mathematical relationships for bistatic spotlight SAR image formation and microdoppler signature extraction based on the Fractional Fourier transform and empirical mode decomposition. An FrFT compute engine will be realised and the algorithms will be tested on simulated and real data.