Abstract
In this paper, a variant of the well known algorithm FastICA is proposed to be used for blind source separation in off-line (block processing) setup and a noisy environment. The algorithm combines a symmetric FastICA with test of saddle points to achieve fast global convergence and a one-unit refinement to obtain high noise rejection ability. A novel test of saddle points is designed for separation of complex-valued signals. The bias of the proposed algorithm due to additive noise can be shown to be asymptotically proportional to σ 3 for small σ, where σ 2 is the variance of the additive noise. Since the bias of the other methods (namely the bias of all methods using the orthogonality constraint, and even of recently proposed algorithm EFICA) is asymptotically proportional to σ 2, the proposed method has usually a lower bias, and consequently it exhibits a lower symbol-error rate, when applied to blind separation of finite alphabet signals, typical for communication systems.
This work was supported by Ministry of Education, Youth and Sports of the Czech Republic through the project 1M0572 and through the Grant 102/07/P384 of the Grant Agency of the Czech Republic.
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Koldovský, Z., Tichavský, P. (2007). Blind Instantaneous Noisy Mixture Separation with Best Interference-Plus-Noise Rejection. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_91
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DOI: https://doi.org/10.1007/978-3-540-74494-8_91
Publisher Name: Springer, Berlin, Heidelberg
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