Analysis (ICA) and Blind Source Separation (BSS) estimated sources signals and the mixing or separating matrices have some special structure or some constraints are imposed for the matrices such as symmetries, orthogonality, non-negativity, sparseness and specified invariant norm of the separating matrix. In this paper we present several algorithms and overview some known transformations which allows us to preserve several important constraints. Computer simulation experiments confirmed validity and usefulness of the developed algorithms. key words: Blind sources separation, independent component analysis with constraints, non-negative blind source separation

{ 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 blind deconvolution task, an allpass lter constraint within the optimization procedure is more appropriate. We show how such constraints can be approximately imposed within gradient adaptive nite-impulse-response (FIR) lter 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. accepted for publication in Journal of VLSI Signal Processing Sys...