一种可用于实时成像的改进PGA算法

摘要

相位梯度自聚焦算法(Phase Gradient Autofocus, PGA)可有效补偿高次相位误差，对实时成像系统获取高分辨图像有重要意义。但是该算法一般需要迭代多次，运算耗时，且在不同场景的应用中算法的聚焦性能不够稳定，这些严重限制了PGA算法在实时处理中的应用。选点和加窗是PGA算法的两个关键步骤，该文提出一种基于数据均值的选点方法和一种基于脉冲包络的窗宽估计方法，这两种方法对数据的自适应能力较强，可使算法获得稳定的聚焦性能，并有效减少迭代次数。实测数据处理结果证实改进的PGA算法可用于实时成像。%The Phase Gradient Autofocus (PGA) algorithm can remove the high order phase error effectively, which is of great significance to get high resolution images in real-time processing. While PGA usually needs iteration, which necessitates long working hours. In addition, the performances of the algorithm are not stable in different scene applications. This severely constrains the application of PGA in real-time processing. Isolated scatter selection and windowing are two important algorithmic steps of Phase Gradient Autofocus Algorithm. Therefore, this paper presents an isolated scatter selection method based on sample mean and a windowing method based on pulse envelope. These two methods are highly adaptable to data, which would make the algorithm obtain better stability and need less iteration. The adaptability of the improved PGA is demonstrated with the experimental results of real radar data.