一种改进选点及加窗处理的相位梯度自聚焦算法

Optimized Phase Gradient Autofocus Algorithm Based on Improved Point Se‐lection and Windowing Processing

相位梯度自聚焦(Phase Gradient Autofocus,PGA)算法被广泛应用于补偿合成孔径雷达(Synthetic Aperture Radar,SAR)图像的相位误差。PGA算法的处理流程中,选点和加窗这两步操作对于算法性能的影响非常大。传统的PGA算法常因选点质量不佳或窗宽估计有误而导致聚焦效果变差,收敛速度变慢。本文提出一种基于能量-信杂比最大准则的强点选择方法和一种自适应窗宽估计方法。利用能量和信杂比两个维度从图像数据中筛选出理想的孤立强散射点,结合并改进两种传统加窗方法自适应地估计窗宽,实现算法稳定性和收敛速度的提升。仿真和实测数据处理结果证实了本文算法的有效性。

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 incorrect 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 algorithm stability and convergence speed.The simulation and experimental data processing results confirm the effectiveness of the algorithm proposed in this paper.