Improved Phase Gradient Autofocus Method for Multi-Baseline Circular Synthetic Aperture Radar Three-Dimensional Imaging - Shiliang Yi; Hongtu Xie; Yuanjie Zhang; Zhitao Wu; Mengfan Ge; Nannan Zhu; Zheng Lu; Pengcheng Qin (Remote Sensing)
Multi-baseline circular synthetic aperture radar (MB CSAR) can be applied to obtain a three-dimensional (3D) image of the observation scene. However, the phase error caused by radar platform motion or atmospheric propagation delay restricts its 3D imaging capabilities. The phase error calibration of MB CSAR data is an essential step in the 3D imaging procedure due to the limited accuracy of positioning sensors. Phase gradient autofocus (PGA) is widely utilized to estimate the phase errors but is subject to shifts in the direction perpendicular to the line of sight and long iteration time in some sub-apertures. In this paper, an improved PGA method for MB CSAR 3D imaging is proposed, which can suppress the shifts and reduce computation time. This method is based on phase gradient estimation, but the prominent units are selected with an energy criterion. Then, weighted phase gradient estimation is presented to suppress the influence of prominent units with poor quality. Finally, a contrast criterion is adopted to reach faster convergence. The experimental results based on the measured MB CSAR data (Gotcha dataset) demonstrate the validity and feasibility of the proposed phase error calibration method for MB CSAR 3D imaging.