随机样本选择的合成孔径雷达距离空变相位梯度自聚焦算法

A range-dependent phase gradient autofocus algorithm integrated stochastic sample selection for SAR imaging

针对距离依赖的相位梯度自聚焦(phase gradient autofocus,PGA)算法中样本选择的问题,本文提出了一种新的基于随机样本选择的距离依赖PGA(range-dependent PGA,RDPGA)算法。不同于传统算法利用固定门限对特显点样本进行硬剔除的选择方式,该算法利用样本的信杂比(signal to clutter ratio,SCR)构造了样本选择概率密度函数,在每次PGA迭代估计过程中,利用该概率密度函数对样本进行随机选择。随机样本选择方法不仅通过增加距离依赖样本的丰富性保证了RDPGA的估计精度,同时还保证了高质量样本在模型参数估计中提供较高贡献,在保持高效性的同时进一步提升了算法的稳健性。实测数据处理结果表明所提算法具有较高的估计精度和稳健性。

Aiming at the sample selection method in the range-dependent phase gradient autofocus(PGA)algorithm,a novel range-dependent PGA(RDPGA)algorithm based on stochastic sample selection is proposed in this paper.Different from the traditional algorithms,in which a fixed threshold is utilized to exclude a part of characteristic point samples,the proposed algorithm uses the samples’signal to clutter ratio(SCR)to construct a probability density function for sample selection.Samples are stochastically selected in each iteration according to the probability density function.Stochastic sample selection ensures the diversity of range cell samples,improving the estimation accuracy of RDPGA.In addition,the probability distribution ensures the high contribution of high-quality samples in the estimation of model parameters.It improves the robustness of convergence while promising a high efficiency.The actual measured data processing results show that the proposed algorithm has high estimation accuracy and robustness.