Geometric optimization methods for adaptive filtering
Geometric optimization methods for adaptive filtering
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Adaptive blind separation of independent sources: a deflation approach
Signal Processing
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Toeplitz And Circulant Matrices: A Review (Foundations and Trends(R) in Communications and Information Theory)
Adaptive paraunitary filter banks for principal and minor subspace analysis
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Self-whitening algorithms for adaptive equalization anddeconvolution
IEEE Transactions on Signal Processing
Allpass filter design and applications
IEEE Transactions on Signal Processing
On gradient adaptation with unit-norm constraints
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Neural networks for blind decorrelation of signals
IEEE Transactions on Signal Processing
Self-stabilized gradient algorithms for blind source separation with orthogonality constraints
IEEE Transactions on Neural Networks
Journal of VLSI Signal Processing Systems
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This paper presents extensions of stochastic gradient independent component analysis (ICA) methods to the blind deconvolution task. Of particular importance in these extensions are the constraints placed on the deconvolution system transfer function. While unit-norm constrained ICA approaches can be directly applied to the prewhitened blind deconvolution task, an allpass filter constraint within the optimization procedure is more appropriate. We show how such constraints can be approximately imposed within gradient adaptive finite-impulse-response (FIR) filter implementations by proper extensions of gradient techniques within the Stiefel manifold of orthonormal matrices. Both on-line time-domain and block-based frequency-domain algorithms are described. Simulations verify the superior performance behaviors provided by our allpass-constrained algorithms over standard unit-norm-constrained ICA algorithms in blind deconvolution tasks.