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论文类型：期刊论文
发表时间：2008-12-01
发表刊物：NEURAL PROCESSING LETTERS
收录刊物：SCIE、EI、Scopus
卷号：28
期号：3
页面范围：209-225
ISSN号：1370-4621
关键字：Blind source extraction; Noisy independent component analysis; Generalized autocorrelations; Gaussian moments; Fetal electrocardiogram
摘要：In the blind source extraction problem, the concept of generalized autocorrelations has been successfully used when the desired signal has special temporal structures. However, their applications are only limited to noise-free mixtures, which is not realistic. Therefore, this paper addresses the extraction of the noisy model based on these temporal characteristics of sources. An objective function, which combines Gaussian moments and generalized autocorrelations, is proposed. Maximizing this objective function, we present a blind source extraction algorithm for noisy mixtures. Simulations on synthesized signals, images, artificial electrocardiogram (ECG) data and the real-world ECG data show the better performance of the proposed algorithm. Moreover, comparisons with the existing algorithms further indicate its validity and also show its robustness to the estimated error of time delay.