Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Blind separation of convolved cyclostationary processes
Signal Processing - Content-based image and video retrieval
Separation of instantaneous mixtures of cyclo-stationary sources
Signal Processing
Practical Optimization: Algorithms and Engineering Applications
Practical Optimization: Algorithms and Engineering Applications
Blind source extraction of periodic signals
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A new source extraction algorithm for cyclostationary sources
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
IEEE Transactions on Signal Processing
Blind source-separation using second-order cyclostationarystatistics
IEEE Transactions on Signal Processing
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The proposed method aims to extract a cyclostationary source, whose cyclic frequency is a priori known, from a set of additive mixtures. The other sources may be either stationary or cyclostationary as long as their cyclic frequencies are different from that of the source to be extracted. The method does not require pre-whitening and consists in minimizing a criterion based on stationary and cyclostationary second order statistics of the observations; this method is labeled as Second Order Cyclostationary Statistics Optimization Criterion (SOC^2). The relevance of this criterion is proven theoretically in the general case of N sources by P sensors, with P=N. Other properties of the algorithm such as its accuracy and its robustness against additive noise or strong interferences are studied through a set of simulations.