Abstract: The purpose of blind source separation (BSS) is to recover the unknown source signals from their linear mixtures without the knowledge of the mixing coefficients. For real-time BSS, the learning rate has an important influence on the algorithm performance. In order to select an appropriate learning rate, this paper proposes an efficient algorithm. According to the dependence between separating signals in current timeslot, the whole signal separation process is divided into two stages: the rapid separation stage and the precise separation stage. A particle swarm optimization method is applied to the rapid separation stage to determine the learning rate, and the learning rate in the precise separation stage is decided by a piecewise function. Simulation experiments demonstrate that significant improvements of  convergence speed and stability are achieved by the proposed algorithm when compared to fixed or other adaptive learning rate methods.

Key words: blind source separation (BSS), learning rate, grading learning, particle swarm optimization

摘要： 盲源分离（blind source separation，BSS）是指在混合系数未知的情况下，从混合信号中恢复出源信号的过程。在实时盲源分离问题中，学习速率的选择对于算法的性能有着至关重要的作用。为了得到合适的学习速率，提出了如下盲源分离的步长选择算法：通过衡量当前时刻输出信号的依赖程度，将整个信号分离过程分为快速分离和精细分离两个阶段。在快速分离阶段，应用粒子群优化算法确定学习速率，而在精细分离阶段，采用分段函数来确定学习速率。仿真结果证实，新算法比使用固定或其他自适应学习速率的算法有更快的收敛速度和更好的稳态性能。

关键词: 盲源分离（BSS）, 学习速率, 分阶段学习, 粒子群优化