Research On The Techniques Of Single-Channel Airborne SAR Ground Moving Target Detection

As a basic fuction of Synthetic Aperture Radar(SAR), Moving target detection plays an important role in both martial field and civilian field.Due to the impact by ground clutter,it is difficult to detect ground moving target,especially ground slow moving target.Moving as well as static target should be researched together.This thesis focuses on ground slow moving target detection technique based on the Single-Channel SAR System.Firstly in this thesis,the development trend of SAR and the phylogeny of airborne SAR ground moving target detection are introduced.The contents of this paper are outlined.Chapter 2 establishes the model of moving target echos,and discusses in detail the effect of target moving on the conventional SAR image.Chapter 3 studies a method of fixed ground clutter cancellation and moving targets detection in single-channel SAR.The data in the SAR image is corrected by subtracting the quadratic phase error,and by adding the same quadratic phase error for comparing,both results are transformed to time domain,where the fixed background clutter have the same image intensity.Then the absolute value of subtracting the two signals is expected to cancel fixed ground clutter and detect moving target,moreover point target and raw data are used in simulation.Based on the investigation in chapter 3,chapter 4 studies the reformative method for detecting moving target in single-channel SAR.Due to adding the band-pass filter aiming at frequency spectrum of moving target,it's expected to have more superior moving target detection performance based on fixed ground clutter cancellation.At last,the arithmetic is validated by point target simulation results.Chapter 5 proposes a new moving target detection method based on correlation coefficient of multi-look SAR images.It analyses the signals of static target and moving target in multi-look images,and introduces a co-registration method.Then the algorithm is analyzed and validated by raw data. Chapter 6 summarizes the whole work of this thesis and points out the problems to be solved in the future.