Abstract
Semi-blind independent component analysis (ICA) incorporates some prior information into standard blind ICA, and thus solves some problems of blind ICA as well as provides improved performance. However, semi-blind algorithms thus far have been much focused on the separation of real-valued signals but little on separation of complex-valued signals. We propose in this paper a semi-blind complex ICA algorithm for extracting a complex-valued source of interest within the framework of constrained ICA. Specifically, magnitude information about the desired signal is utilized as inequality constraint to the cost function of kurtosis maximization algorithm, which is an efficient complex ICA algorithm for separating circular and noncircular sources. The simulation results demonstrate that the proposed algorithm can extract a desired complex signal with much improved performance and robustness.
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References
Comon, P.: Independent Component Analysis, a new concept? Signal Proc. 36, 287–314 (1994)
Lu, W., Rajapakse, J.C.: ICA with Reference. In: 3rd International Conference on Independent Component Analysis and Blind Source Separation (ICA2001), pp. 120–125 (2001)
Lu, W., Rajapakse, J.C.: Approach and Applications of Constrained ICA. IEEE Trans. Neural Nets 16, 203–212 (2005)
Sawada, H., Mukai, R., Araki, S., Makino, S.: Frequency-domain Blind Source Separation, Speech Enhancement. Springer, New York (2005)
Calhoun, V.D., Adali, T., Pearlson, G.D., Van Zijl, P.C., Pekar, J.J.: Independent Component Analysis of fMRI Data in the Complex Domain. Magn. Resonance Med. 48, 180–192 (2002)
Cardoso, J.F., Souloumiac, A.: Blind Beamforming for Non Gaussian signals. IEEE Proc. Radar Signal Proc. 140, 362–370 (1993)
Eriksson, J., Koivunen, V.: Complex-valued ICA using Second Order Statistics. In: 14th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, Sao Luis, Brazil, pp. 183–191 (2004)
Bingham, E., Hyvärinen, A.: A fast fixed-point Algorithm for Independent Component Analysis of Complex Valued Signals. Int. J. Neural Systems 10, 1–8 (2000)
Li, H., Adali, T.: A Class of Complex ICA Algorithms Based on Kurtosis Maximization. IEEE Trans. Neural Nets 19, 408–420 (2008)
Li, H., Adali, T.: Gradient and Fixed-Point Complex ICA Algorithms Based on Kurtosis Maximization. In: 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, Maynooth, Ireland, pp. 85–90 (2006)
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Chen, JY., Lin, QH. (2008). A Semi-blind Complex ICA Algorithm for Extracting a Desired Signal Based on Kurtosis Maximization. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_87
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DOI: https://doi.org/10.1007/978-3-540-87734-9_87
Publisher Name: Springer, Berlin, Heidelberg
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