一种新的基于遗传算法的压缩感知重构方法 及其在SAR 高分辨距离像重构中的应用

首先提出一种基于遗传算法的压缩感知重构新方法,并设计了具体的算法流程.该方法运用遗传迭代思想,在稀疏度未知的情况下可准确重构出原始信号,避免了子空间跟踪问题.在此基础上,进一步将所提新方法应用于合成孔径雷达(SAR)高分辨距离像的重构,同时建立了相关的SAR系统模型,构造了有效的稀疏变换矩阵和观测矩阵.仿真结果表明了所提出方法的有效性,同时验证了该方法用于SAR高分辨距离像重构是可行的和鲁棒的.

A Novel Reconstruction Method Based on Genetic Algorithm in CS Theory and Its Application in SAR HRRP Reconstruction

First of all, a novel reconstruction method in compressed sensing(CS) theory is proposed based on genetic
algorithm(GA), and its concrete flow is designed. In the method, the genetically iterative idea is adopted to reconstruct the
original signal exactly on the condition that the sparsity degree is unknown, which avoids the subspace pursuit. Furthermore,
the proposed reconstruction method is employed in high resolution range profile(HRRP) reconstruction in synthetic aperture
radar(SAR) imaging, and related SAR application model is established. In this model, the effective sparsity transform matrix
and measurement matrix are designed successfully. The simulation results not only show the effectiveness of the proposed
reconstruction method, but also verify that the reconstruction of SAR HRRP by using the proposed method is feasible and
robust.