基于改进遗传算法的正交匹配追踪信号重建方法

针对压缩传感现有重建算法的缺陷:重建速度慢，在给定迭代次数的条件下进行重建，缺乏自适应性等，提出了一种改进的遗传算法与正交匹配追踪算法相结合的方法来构造重建矩阵。首先采用改进的遗传算法从测量矩阵的列中以最优染色体的形式选出与当前冗余向量最大程度相关的列,然后从测量矩阵中减去最优染色体部分并反复迭代,直到满足重建精度。实验结果表明，与现有的重建算法相比,在满足相同的重建精度条件下,该方法所需要的重建时间减少了5 s左右,所需要的测量矩阵规模减小了约10%,而且能在待重建信号稀疏度未知时自适应地控制迭代停止时间。

Orthogonal matching pursuit signal reconstruction based on improved genetic algorithm

The core problem of compressed sensing theory is how to find an efficient and fast reconstruction algorithm. The existing reconstruction algorithms (such as orthogonal matching pursuit) have some defects: slow reconstruction, the reconstruction algorithm is carried out under a given number of iteration conditions, and the adaptation is reduced by this compulsory stop. An improved genetic algorithm (IGA) combining with orthogonal matching pursuit (OMP) algorithm is carried out to construct the reconstruction matrix. First, an improved genetic algorithm is used to select the current maximum redundancy column vector from the measurement matrix columns with an optimal chromosome method. Then subtract the part of columns with optimal chromosome from the measurement matrix, and repeat iteration until it meets the reconstruction accuracy. Simulation results show that, compared with the existing reconstruction algorithms under the same conditions, time consuming of the algorithm reduces 5 s and the size of the measurement matrix reduces about 10%. This method can stop iteration adaptively under the condition of reconstruction signal with unknown sparseness.