The Phase Gradient Autofocus(PGA)algorithm is widely used to compensate for phase errors in Synthetic Aperture Radar(SAR)images.In the processing flow of the PGA algorithm,the two-step operation of selecting points and adding windows has a significant impact on algorithm performance.Traditional PGA algorithms often suffer from poor point selection quality or in-correct window width estimation,leading to poor focusing effect and slower convergence speed.This article proposes a strong point selection method based on the maximum energy signal-to-noise ratio criterion and an adaptive window width estimation method.Using the dimensions of energy and signal-to-noise ratio,ideal isolated strong scattering points are selected from image data,and two traditional windowing methods are combined and improved to adaptively estimate the window width,achieving improved algo-rithm stability and convergence speed.The simulation and experimental data processing results confirm the effectiveness of the algo-rithm proposed in this paper.
关键词
自聚焦/相位梯度自聚焦算法/强点选择/窗宽估计
Key words
Autofocus/Phase gradient autofocus/Strong point selection/Window width estimation
维普
万方数据