Co-registered Vibrometry and Imaging: A Combined Synthetic-Aperture Radar and Fractional-Fourier Transform Approach [29U08UNMhaya]

该项目的目标是引入一种革命性的合成孔径雷达（SAR）成像方法，该方法将共同注册的振动传感与高分辨率成像相结合。合成孔径雷达平台是解决这一问题的天然工具，因为（1）它已被证明是一种高效的成像技术，（2）它本身就能够感知物体电磁回波中振动引起的多普勒频移。一个强大的信号处理工具，称为分数傅里叶变换（FRFT），利用在这个项目中作为一个框架，设计一种新的多脉冲，多啁啾SAR成像策略，产生的振动频率（频谱图）叠加在高分辨率SAR图像的空间地图。将开发新的基于子空间的估计算法，采用FRFT框架，以获得高分辨率的啁啾率和中心频率的估计是统计上一致的和渐近最佳的，这些估计翻译，反过来，到估计的振动物体的瞬时速度被照亮的SAR成像系统。将开发一个简单的实验室平台来演示拟议的传感概念。还将研究所提出的方法产生的签名的基础上识别结构的逆问题。在这个项目中，一名本科生将被提名获得NCMR本科生奖学金，并将通过我们与桑迪亚国家实验室的合作，努力为我们的学生创造实习机会。研究成果将被整合到两个当前的课程在新墨西哥州的大学：研究生水平的数字信号处理课程和高级选修课程题为雷达信号处理。

The objective of this project is to introduce a revolutionary approach for synthetic-aperture-radar (SAR) imaging that uniquely combines co-registered vibration sensing with high-resolution imaging. The SAR platform is a natural fit to the problem because (1) it has already been proven as a highly effective imaging technology and (2) it is inherently capable of sensing vibration-induced Doppler shifts in the electromagnetic returns from objects. A powerful signal-processing tool, called the fractional Fourier transform (FRFT), is exploited in this project as a framework to design a novel multi-pulse, multi-chirp SAR imaging strategy that yields a spatial map of vibration frequencies (spectrograms) superimposed on high-resolution SAR imagery. New subspace-based estimation algorithms will be developed that employ the FRFT framework to obtain high-resolution chirp-rate and center-frequency estimates that are statistically consistent and asymptotically optimal; these estimates translate, in turn, into estimates of the instantaneous velocity of the vibrating objects that are illuminated by a SAR imaging system. A simple laboratory platform to demonstrate the proposed sensing concept will be developed. The inverse problem of identifying structures based upon signatures generated by the proposed approach will also be studied. During this project, one undergraduate student will be nominated to receive the NCMR Undergraduate Scholarship and effort will be made through our collaboration with the Sandia National Laboratories to create internship opportunities for our students. Research results will be integrated into two current courses at the University of New Mexico: a graduate-level Digital Signal Processing course and a senior-level elective course titled Radar Signal Processing.