Super-resolution Imaging Of Complex Data Processing Method Based On Compressed Sensing, Synthetic Aperture Radar

Synthetic aperture radar is a all-day, all-weather, large-area and high resolution imaging radar. With its military and national economic aspects of the widespread application, research of improve its imaging resolution ratio become more and more high attention, different scholars respectively from both software and hardware conducted fruitful attempt. Regularization method is one of technical to improve synthetic aperture radar resolution through data processing, but regularization model optimization algorithm usually require a solution in a real data range, while for the plural of SAR echo data is powerless, combining with our achievements in field of signal sparse decomposition and reconstruction, this paper presents a way to solve this problem, it is further extensions on the basis of our original research.In this paper the main innovation points include:First, relying on our research team of in-depth research achievements in the field of signal image sparse representation, will compressed sensing applied to regularization model, and then puts forward the concept of imaging operator, precise modeling for one-dimensional distance, orientation and 2-d system respectively, will SAR imagery problem into a signal reconfiguration problem.Secondly, according to the Turkish scholars Cetin proposed alternating iterative thoughts in 2009, will echo reconstruction divided into two parts of phase and amplitude, which can be alternant processed, realized the complex data optimization algorithm of the synthetic aperture radar regular model.This paper based on the experiment, all conclusions have been proved through a lot of experiments, in order to facilitate realize data simulation, specialized implement the visualization, real-time adjust scene parameters application software in matlab environment.