Cyclostationarity: half a century of research
Signal Processing
Bibliography on cyclostationarity
Signal Processing
Separation of instantaneous mixtures of cyclo-stationary sources
Signal Processing
On blind MIMO system identification based on second-order cyclic statistics
Research Letters in Signal Processing
Blind Separation of Cyclostationary Signals
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Blind source separation based on self-organizing neural network
Engineering Applications of Artificial Intelligence
Blind separation of cyclostationary sources using joint block approximate diagonalization
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Sequential high-resolution direction finding from higher order statistics
IEEE Transactions on Signal Processing
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Multihomogeneous polynomial decomposition using moment matrices
Proceedings of the 36th international symposium on Symbolic and algebraic computation
Generic blind source separation using second-order local statistics
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
General tensor decomposition, moment matrices and applications
Journal of Symbolic Computation
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Most of the second-order (SO) and higher order (HO) blind source separation methods developed in the 1990s aim at blindly separating statistically independent sources that are assumed zero-mean, stationary, and ergodic. Nevertheless, in many situations of practical interest, such as in radiocommunication contexts, the sources are nonstationary and very often (quasi)-cyclostationary (digital modulations). In these conditions, it becomes important to wonder whether the performance of these current SO and HO blind source separation methods, which have been developed for stationary sources, may be affected by the potential nonstationarity of the latter. Limiting the analysis to the SO and fourth-order (FO) cumulant-based blind source separation methods, the purpose of this paper is to bring some answers to this important question through the behavior analysis of the empirical SO and FO cumulants estimator in the presence of zero-mean (quasi)-cyclostationary sources