Adaptive blind separation with an unknown number of sources
Neural Computation
A contrast function for independent component analysis without permutation ambiguity
IEEE Transactions on Neural Networks
A two-stage Independent Component Analysis-based method for blind detection in CDMA systems
Digital Signal Processing
Adaptive weighted orthogonal constrained algorithm for blind source separation
Digital Signal Processing
| Hi-index | 35.68 |
In this paper, the problem of the blind separation of independent sources is considered. Our approach relies on high-order inverse criteria. After generalizing the definition of classical contrast functions, we exhibit a wide class of generally nonsymmetrical functions that will be called “generalized contrasts” and whose maximization is proved to be a sufficient condition for source separation. We also establish a connection with “cumulant matching,” showing an equivalence between the two approaches. Then, in the case of two sources, a statistical study of the estimated parameter based on one of these new contrasts is presented. Finally, computer simulations illustrate the results and demonstrate all the interest we can find in considering a nonsymmetrical contrast