盲信号分离自适应算法研究

Research on Adaptive Algorithm of Blind Signal Separation

盲信号分离是信号处理领域的一个重要问题.其目的是当满足一定假设条件后,根据观测到的混合信号还原分离出若干原始信号.阐述了盲信号分离的模型和原理,分析了几种RLS和LMS盲信号分离自适应算法的性能,并对上述算法进行了仿真比较.仿真结果表明RLS算法比LMS算法收敛速度快,但LMS比RLS稳定性好.

The topic of blind signal separation（ BSS） is an important research hotpot in signal processing. The purpose of BSS is to recover the original independent signals from their mixed observation date. In this paper,we expatiate on the model and theory of BSS,and analyze some adaptive algorithms—LMS and RLS. The performance and effect of these algorithms are compared,and the results show that the RLS has batter convergence than LMS,but the stability of RLS is worse than LMS.