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Complex-valued independent vector analysis: Application to multivariate Gaussian model
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DOI：10.1016/j.sigpro.2011.09.034.png)
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摘要
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We consider the problem of joint blind source separation of multiple datasets and introduce a solution to the problem for complex-valued sources. We pose the problem in an independent vector analysis (IVA) framework and provide a new general IVA implementation using Wirtinger calculus and a decoupled nonunitary optimization algorithm to facilitate Newton-based optimization. Utilizing the noncircular multivariate Gaussian distribution as a source prior enables the full utilization of the complete second-order statistics available in the covariance and pseudo-covariance matrices. The algorithm provides a principled approach for achieving multiset canonical correlation analysis. (c) 2011 Elsevier B.V. All rights reserved.
Keyword:
Canonical correlation analysis (CCA)
Independent vector analysis (IVA)
Complex-valued signal processing
期刊
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3.6
论文数:
10.0K
被引数:
1.7W
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