IEEE Transactions on Signal Processing
Underdetermined blind source separation based on relaxed sparsity condition of sources
IEEE Transactions on Signal Processing
Blind underdetermined mixture identification by joint canonical decomposition of HO cumulants
IEEE Transactions on Signal Processing
Underdetermined blind separation of non-sparse sources using spatial time-frequency distributions
Digital Signal Processing
Blind multipath MIMO channel parameter estimation using the Parafac decomposition
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
A new blind method for separating M+1 sources from M mixtures
Computers & Mathematics with Applications
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
SIAM Journal on Matrix Analysis and Applications
Blind separation of non-stationary sources using continuous density hidden Markov models
Digital Signal Processing
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In this paper we study two fourth-order cumulant-based techniques for the estimation of the mixing matrix in underdetermined independent component analysis. The first method is based on a simultaneous matrix diagonalization. The second is based on a simultaneous off-diagonalization. The number of sources that can be allowed is roughly quadratic in the number of observations. For both methods, explicit expressions for the maximum number of sources are given. Simulations illustrate the performance of the techniques