Blind separation of convolved cyclostationary processes
Signal Processing - Content-based image and video retrieval
On blind MIMO system identification based on second-order cyclic statistics
Research Letters in Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Identifiability of a band-limited system from its cyclostationaryoutput autocorrelation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
ARMA system identification based on second-order cyclostationarity
IEEE Transactions on Signal Processing
Blind source-separation using second-order cyclostationarystatistics
IEEE Transactions on Signal Processing
Blind MIMO FIR channel identification based on second-order spectra correlations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Frequency domain blind MIMO system identification based on second and higher order statistics
IEEE Transactions on Signal Processing
Blind channel identification based on second-order statistics: a frequency-domain approach
IEEE Transactions on Information Theory
EURASIP Journal on Wireless Communications and Networking
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This article introduces a new frequency domain approach for either MIMO system identification or source separation of convolutive mixtures of cyclostationary signals. We apply the joint diagonalization algorithm to a set of cyclic spectral density matrices of the measurements to identify the mixing system at each frequency bin up to permutation and phase ambiguity matrices. An efficient algorithm to overcome the frequency-dependent permutations and to recover the phase, even for non-minimum-phase channels, based on cyclostationarity is also presented. The new approach exploits the fact that each input signal has a different and specific cyclic frequency. Simulation examples are presented to illustrate the effectiveness of this approach.