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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.
Firstly, a new compressive sensing reconstruction method based on genetic algorithm is proposed and a specific algorithm flow is designed.This method uses genetic iteration to accurately reconstruct the original signal with unknown sparsity, and avoids subspace tracking Problem.On the basis of this, the new method is further applied to the reconstruction of synthetic aperture radar (SAR) high resolution range image, and the related SAR system model is established, and the effective sparse transformation matrix and observation matrix are constructed. The results show the effectiveness of the proposed method, and verify that this method is feasible and robust for SAR high-resolution image reconstruction.