Matrix computations (3rd ed.)
Independent component analysis: algorithms and applications
Neural Networks
Blind Channel Equalization and Identification
Blind Channel Equalization and Identification
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Multiple Composite Hypothesis Testing: A Competitive Approach
Journal of VLSI Signal Processing Systems
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Subspace methods for the blind identification of multichannel FIRfilters
IEEE Transactions on Signal Processing
Prediction error method for second-order blind identification
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
Blind identification and equalization based on second-order statistics: a time domain approach
IEEE Transactions on Information Theory
Blind channel identification based on second-order statistics: a frequency-domain approach
IEEE Transactions on Information Theory
A comparison of deconvolution techniques for the ultrasonic nondestructive evaluation of materials
IEEE Transactions on Image Processing
Blind sparse source separation using cluster particle swarm optimization technique
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
A robust blind sparse source separation algorithm using genetic algorithm to identify mixing matrix
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
A robust blind sparse source separation algorithm using genetic algorithm to identify mixing matrix
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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In this paper, we present a computationally efficient algorithm which provides a general solution to blind inverse problems for sparse input signals. The method takes advantage of the clustering typical of sparse input signals to identify the channel matrix, solving four problems sequentially: detecting the number of input signals (i.e. clusters), estimating the directions of the clusters, estimating their amplitudes, and ordering them. Once the channel matrix is known, the pseudoinverse can be used as the canonical solution to obtain the input signals. When the input signals are not sparse enough, the algorithm can be applied after a linear transformation of the signals into a domain where they show a good degree of sparsity. The performance of the algorithm for the different types of problems considered is evaluated using Monte Carlo simulations.