摘要
目的为更加有效地处理强弱信号混合这一特殊盲源分离问题。方法根据阵列信号处理模型与盲源分离模型之间的一致性,以最小输出能量为准则推导了相应的约束条件,并求得线形约束最小方差下的解,即对真实信源的估计;实验中采用强背景噪声EEG与诱发脑电(EP)作为源信号,利用本文方法对其混合信号进行处理。结果该方法能够有效地从强背景噪声EEG中将弱信号EP提取出来,具有很好的有效性和鲁棒性。结论与独立分量分析等经典的盲源分离方法相比,该算法不需要求解解混矩阵,计算量小,在低信噪比情况下能够准确地估计出信源。
Objective To deal with blind source separation（BSS） more effectively in the field of mixed signal separations of strong and week sources.Methods According to the consistency between array signal processing model and BSS model,the real sources were estimated under linear constrains and least mean square（LMS）,based on minimum output energy（MOE）.EEG and evoked potential（EP） were used as strong background noise and week signal source separately in our experiment.The mixed signals were separated with the method proposed in this paper.Results The EP could be seperated from the strong noise EEG effectively.Conclusion Compared with typical BSS approaches,this new algorithm need not solve the unmixing matrix,so it runs fast,is of a little low computational complexity and can correctly estimate the weak signal source from low signal/noise（S/N） ratio.
出处
《航天医学与医学工程》
CAS
CSCD
北大核心
2010年第3期218-223,共6页
Space Medicine & Medical Engineering
基金
国家自然科学基金(30570475
60872122
60940023)
教育部博士点基金(20050141025)
关键词
波束形成
盲源分离
最小输出能量
beamform
blind source separation
minimum output energy
