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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.Abstract: To speed conventional Adaptive Eigenvalue Deposition Algorithm (AEDA), an NLMSbased optimized AEDA algorithm was proposed.It gives better performance for convergence,which makes it possible for realtime applications.Experimental results showed that this method can work well in sound source localization.
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