Study Of Super-resolution Algorithms For Inverse Synthetic Aperture Radar

Inverse Synthetic Aperture Radar(ISAR) is a high resolution imaging radar. It has a wide application prospects and development potential in detection, tracking and identification of moving target.To ISAR, the higher the resolution of image has, the higher the rate of follow-up target detection and identification would be obtained. This thesis starts from improving image resolution, basing on the establishment of ISAR imaging model and analysis of imaging principle. Firstly, Polar Format Algorithm(PFA) and the interpolation approach under the large corner are discussed. Simulations show that PFA can effectively eliminate image blurring caused by migration through resolution cell. Secondly, Estimation of Signal Parameter via Rotational Invariance Technique(ESPRIT) super-resolution imaging algorithm is presented in cross range process that can recover harmonic parameters from echo signal. Finally, one-dimensional Multiple Signal Classification Algorithm(MUSIC) is introduced and extended to two-dimensional. Super-resolution images in down range and cross range can be received with two-dimensional MUSIC.