Bootstrap:一种快速盲自适应信号分离器

[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing Pub Date : 1992-03-23 DOI:10.1109/ICASSP.1992.226054

A. Dinc, Yeheskel Bar-Ness

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引用次数: 30

摘要

提出了一种用于多信号分离的快速多维自适应算法Bootstrap。它分离了多个相互强加的不相关信号。自适应算法不需要训练序列，它使用基于输出信号相关性最小化的优化准则。在不同的特征值扩展情况下，将该算法与最小均方(LMS)算法的学习过程进行了比较。计算机仿真结果表明，Bootstrap算法的收敛速度比LMS算法快得多。Bootstrap算法的学习过程几乎不受特征值扩展的影响

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Bootstrap: a fast blind adaptive signal separator

A fast multidimensional adaptive algorithm, Bootstrap, is proposed for multiple signal separation. It separates multiple uncorrelated signals imposed on each other. The bootstrap adaptive algorithm, which does not require training sequences, uses an optimization criteria that is based on minimization of output signal correlations. The learning process of this algorithm is compared with that of the least mean square (LMS) algorithm for different eigenvalue spreads. It has been found from computer simulations that the Bootstrap algorithm converges much faster than the LMS algorithm. The learning process of the Bootstrap algorithm is almost independent of eigenvalue spread.<>

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[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing

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