针对快速独立分量分析（FastICA）算法提取多个同频混叠信号时的初值选择敏感性和收敛性能差的问题，提出了一种双松弛因子改进的FastICA（DLM-FastICA）算法。先在牛顿迭代法中引入双松弛因子，通过自适应调节分离矩阵的组合系数得到最优权值分离矩阵，从而改善FastICA算法的初值敏感性；再利用改进的FastICA（M-FastICA）算法的快速收敛特性提取信号，提高算法的分离精度和收敛速度。仿真结果表明，该算法使得提取信号与源信号的相似系数达到0.9，同时迭代次数较原算法减少近40%，具有更加快速、稳定的提取性能。

For solving the problems of poor initial value selection sensitivity and weak convergence performance while using fast independent component analysis (FastICA) algorithm to extract co-frequency aliased signals, a double relaxation factors modified FastICA (DLM-FastICA) algorithm is proposed. Firstly, the double relaxation factor is introduced into Newton iteration method, and the optimal weight separation matrix is obtained by adjusting the combination coefficient of separation matrix adaptively, then the sensitivity of FastICA algorithm to the initial value is improved; furthermore, the extraction signal is obtained via fast convergence characteristics of modified FastICA (M-FastICA), and the separation accuracy and convergence speed of the algorithm are improved. The simulation results show that the similarity coefficient between the extracted signal and the source signal reaches 0.9, meanwhile compared to the original algorithm, the iteration times are reduced by nearly 40%, so the proposed algorithm has faster and more stable extraction performance.