Research On Inverse Synthetic Aperture Radar Imaging

When radar performing target detection,some partial scattering points different from main-motion status on targets will perform Doppler frequency modulation effects on echo signals,causing spectrum broadening or side lobes.This phenomenon is called micro-Doppler effect.At this time,the resolution of the conventional ISAR imaging algorithm is lowered,and the ideal imaging result.With the competition of space resources among countries,there is a high strategic value for the research on the ISAR imaging technology of the micro-motion target.It can be applied to ballistic missile defense and space target surveillance.The paper focuses on three aspects in terms of ISAR target imaging technology,sparse aperture ISAR imaging technology and precession target ISAR imaging technology with micro-motion components.The main contents are as follows:In the second chapter,the research on ISAR target imaging technology with micro-motion components is first carried out.Based on utilizing traditional approach of spectrum rearrangment to remove micro-Doppler within range cells,a pseudo matched filter algorithm is introduced to remove the micro-motion among the range cells,and obtain the same imaging result as the traditional MSBL.Then,considering that the spectrum is sparse subject signal during rearrangement,a sparse recovery method based on Gamma-Laplace prior is proposed.Finally,simulation data verifies the effectiveness of the sparse recovery algorithm,and the resultperformance is better than the traditional SBL method.At the same time,the chapter also designs the anti-noise experiment to validate certain anti-noise performance of proposed algorithm while obtaining satisfactory imaging results under the condition of-5dB.The third chapter investigates the sparse aperture ISAR imaging technology.Considering the practical scene,the micro-motion information will bring complex modulation information to the target echo,reducing the effective arc segment for imaging,and make the echo data appear as sparse aperture.In order to solve this problem,this chapter empolys the Gamma-Laplace based prior complete Bayesian inference to sparsely reconstruct the ISAR image,and introduces the maximum contrast criterion to estimate the initial phase error matrix in reconstruction process to achieve the autofocus.Finally,the imaging result of simulation and measured data demonstrate effectiveness of the sparse aperture imaging algorithm proposed in this chapter.Further,the ISAR imaging of short-arc precession target based on sparse Bayesian is studied in the fourth chapter.Firstly,the traditional precession target imaging is modeled and analyzed.Based on characteristics of the echo model,the precession angle within short arc segment converting to a small angle is assumed,then it is equivalent to the problem of non-uniform rotational imaging.Considering the sinusoid feature of angular velocity variation,the problem is modeled as a uniform acceleration rotation imaging problem under sparse constraints.Subsequently,adopting the Gamma-Laplace based prior sparse recovery method proposed in Chapter 2 to sparsely reconstruct the observed short arc segment data.Finally,the rationality of the proposed method is certified by simulation and darkroom data,and obtained imaging results are superior to the traditional RID algorithm.