期刊名称：EURASIP Journal on Advances in Signal Processing
印刷版ISSN：1687-6172
电子版ISSN：1687-6180
出版年度：2019
卷号：2019
期号：1
页码：1-12
DOI：10.1186/s13634-019-0622-8
出版社：Hindawi Publishing Corporation
摘要：Constrained independent component analysis (ICA) is an effective method for solving the blind source separation with a prior knowledge. However, most constrained ICA algorithms are proposed for the real-valued sources. In this paper, a novel constrained noncircular complex fast independent component analysis (c-ncFastICA) algorithm based on the fixed-point learning is proposed to address the complex-valued sources. The c-ncFastICA algorithm uses the augmented Lagrangian method to obtain a new cost function and then utilizes the quasi-Newton method to search its optimal solution. Compared with other ICA and constrained ICA algorithms, c-ncFastICA has better separation performance. Simulations confirm the effectiveness and superiority of the c-ncFastICA algorithm.
