Array Signal Processing: Concepts and Techniques
Array Signal Processing: Concepts and Techniques
Extraction of Specific Signals with Temporal Structure
Neural Computation
Cyclostationarity: half a century of research
Signal Processing
A fast algorithm for one-unit ICA-R
Information Sciences: an International Journal
EURASIP Journal on Applied Signal Processing
Complex blind source extraction from noisy mixtures using second-order statistics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Independent component analysis by entropy bound minimization
IEEE Transactions on Signal Processing
A method for ICA with reference signals
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
On Extending the Complex FastICA Algorithm to Noncircular Sources
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind Extraction of Dominant Target Sources Using ICA and Time-Frequency Masking
IEEE Transactions on Audio, Speech, and Language Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Approach and applications of constrained ICA
IEEE Transactions on Neural Networks
A New Constrained Independent Component Analysis Method
IEEE Transactions on Neural Networks
Fast Independent Component Analysis Algorithm for Quaternion Valued Signals
IEEE Transactions on Neural Networks - Part 1
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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.