摘要
针对传统的自适应特征值分解(AEDA)的延估计算法收敛时间慢的问题,提出一种改进的AEDA自适应算法,该方法将归一化最小均方法与AEDA相结合,加快了收敛速度,使其可应用于信号的实时处理.实验结果证明,在真实声场中,该算法能够用于声源定位.
To speed conventional Adaptive Eigenvalue Deposition Algorithm （AEDA）, an NLMS-based optimized AEDA algorithm was proposed. It gives better performance for convergence,which makes it possible for real-time applications. Experimental results showed that this method can work well in sound source localization.
出处
《华东师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2007年第5期113-117,共5页
Journal of East China Normal University(Natural Science)
