基于LBI的二维复稀疏信号重建算法及应用研究

针对二维复稀疏信号重建时存在存储空间和计算复杂度增加的问题, 本文提出了一种快速并行重建二维复稀疏信号的并行线性Bregman迭代(Parallel fast linearized Bregman iteration, PFLBI)算法. 首先, 构建了二维复稀疏信号的结构模型以及PFLBI算法基本迭代格式; 其次, 通过变步长方式提高迭代收敛速度, 而每次迭代的步长则是通过估计中间变量的积累量突破收缩阈值需要的积累步数得到的; 再次, 对算法的性能指标进行了分析; 最后, 将该算法应用于逆合成孔径雷达(Inverse synthetic aperture radar, ISAR)成像. 实验结果表明该算法在重建性能和速度上具有优势.

2D Complex Sparse Reconstruction Algorithm with LBI and Its Application

A parallel fast linearized Bregman iteration (PFLBI) algorithm is proposed to solve the problem of large storage and complex computation in the reconstruction of 2D sparse signal. The PFLBI algorithm can efficiently reconstruct 2D sparse signal in a parallel way. Firstly, the matrix form of linearized Bregman iteration (LBI) is constructed. Secondly, the convergence speed is improved by estimating the number of the steps for intermediate variables to cross the shrinkage threshold. Thirdly, the performance of the proposed algorithm is analyzed. Finally, PFLBI is applied to inverse synthetic aperture radar (ISAR) imaging. Experimental results show that the proposed algorithm can improve the performance and the speed of reconstruction.