Optimized Phase Gradient Autofocus Algorithm Based on Improved Point Se-lection and Windowing Processing
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.
AutofocusPhase gradient autofocusStrong point selectionWindow width estimation
万方数据
维普
