Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations |
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| Authors: | Hongjuan Zhang Zhenwei Shi Chonghui Guo Enmin Feng |
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| Affiliation: | (1) Department of Applied Mathematics, Dalian University of Technology, Dalian, 116024, People’s Republic of China;(2) Image Processing Center, School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing, 100083, People’s Republic of China;(3) Institute of Systems Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China |
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| Abstract: | 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. |
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| Keywords: | Blind source extraction Noisy independent component analysis Generalized autocorrelations Gaussian moments Fetal electrocardiogram |
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