Synthetic Aperture Radar (SAR) is an all-weather radar imaging system that utilizes the movement of the radar platform to create high resolution images of the scene . The high range resolution is derived by transmitting wideband linear frequency modulated (LFM) signal , and appropriate compression of the received echoes . As an alternate , stepped frequency LFM signal can be applied which not only provides high range resolution , but also reduces the instantaneous bandwidth and sampling rate . Ideally, it is assumed that the radar platform moves at a constant velocity along a nominal trajectory. In practice, trajectory deviations of the platform cause serious phase errors that impair the focusing quality of SAR images. Most of these errors are compensated using a navigation system . However , residual phase errors due to uncompensated platform motion , measurement model mismatch , and measurement noise can cause degradations in SAR image reconstruction . Therefore , an autofocus method is usually adopted for high-resolution SAR imaging . Autofocus methods derive the required parameters for error cancellation from the SAR raw data . Several successful approaches have been presented for the SAR autofocus in fully sampled scenarios . However , these traditional approaches may not be suitable for undersampled SAR . Recently , some researchers apply compressed sensing (CS) tools to SAR phase error compensation . Unlike traditional autofocus approaches , CS-based methods can operate on undersampled data to reconstruct the SAR image where it is sparse in a certain transform domain . This thesis considers a SAR system with stepped-LFM signaling and co-prime sampling in spotlight imaging mode and discusses SAR image autofocusing using CS concept . The existing approaches are reviewed and a novel autofocus method is proposed . Moreover , the performance of the proposed method is compared to that of the existing ones through computer simulation . Simulation results demonstrate the good ability of the proposed method for phase error correction and image reconstruction in different scenarios . Keywords : Synthetic Aperture Radar (SAR), Stepped-LFM, Phase error, Autofocus, Compressed sensing
