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Changsha, China

Wang X.,Deya Road 50 | Huang Z.,Deya Road 50 | Zhou Y.,Deya Road 50 | Ren X.,Deya Road 50
Neurocomputing | Year: 2013

Signals of interest (SOIs) extraction are a vital issue in the field of communication signal processing. A promising approach is constrained independent component analysis (cICA). This paper extends the conventional constrained independent component analysis framework to the case of complex-valued mixing model and presents different prior information and different ways to be incorporated into the cICA framework. Two examples are demonstrated, ICA with cyclostationary constraint (ICA-CC) and ICA with spatial constraint (ICA-SC). The adaptive solution using the gradient ascent learning process is derived to solve the new constrained optimization problem in the ICA-CC example, while the rough spatial information corresponding to the direction of arrival (DOA) of the SOI can be utilized to select the specific initial vector for the desired solution before the learning process in the ICA-SC example. The corresponding experiment results show the efficacy and accuracy of the proposed algorithms. © 2012 Elsevier B.V. Source

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