In this paper we propose extension of multichannel blind equalization problem as suming that both mixing and demixing models are described by stable linear state space systems The problem is formulated as an optimization task New learning algorithms are developed which can be considered as extension of existing algorithms By applying demixing state space model we will be able to reduce complexi...

In performing blind deconvolution to remove reverberation from speech signal, most acoustic deconvolution filters need a great many number of taps, and acoustic environments are often time-varying. Therefore, deconvolution filter coefficients should find their desired values with limited data, but conventional methods need lots of data to converge the coefficients. In this paper, we use sparse ...

Astronomical images taken by ground-based telescopes suffer degradation due to atmospheric turbulence. This degradation can be tackled by costly hardware-based approaches such as adaptive optics, or by sophisticated software-based methods such as lucky imaging, speckle imaging, or multi-frame deconvolution. Software-based methods process a sequence of images to reconstruct a deblurred high-qual...

The aim of this Letter is to present a preliminary study on intrinsicallystable discrete-time 2-pole IIR adaptive filtering for blind equalization based on minimum-entropy deconvolution. The structural-adapting theory is developed and numerical experimental results are discussed which illustrate the soundness of the proposed theory: The adaptive filter keeps stable and good deconvolution result...

This correspondence shows that Shalvi and Weinstein’s blind deconvolution criteria are applicable for finite SNR regardless of channels having zeros on the unit circle or not. The associated deconvolution filter is stable with a nonlinear relation to the nonblind MMSE equalizer and capable of performing perfect phase equalization for finite SNR.

A number of ill-posed inverse problems in signal processing, like blind deconvolution, matrix factorization, dictionary learning and blind source separation share the common characteristic of being bilinear inverse problems (BIPs), i.e. the observation model is a function of two variables and conditioned on one variable being known, the observation is a linear function of the other variable. A ...

A new update equation for the general multichannel blind deconvolution (MCBD) of a convolved mixture of source signals is derived. It is based on the update equation for blind source separation (BSS), which has been shown to be an alternative interpretation [1] of the natural gradient applied to the minimization of some mutual information criterion [2]. Computational complexity is held at a min...

It is well known that blind channel deconvolution enables the receiver to equalize the channel simply by analyzing the received digital signal. Much of the work in 1990’s faces the challenge presented by multiple-output systems, exploiting cyclostationarity properties and multivariate formulation of the incoming data. Our proposal is twofold: on one hand, we develop a theoretical analysis of a ...

Single-frame multichannel blind deconvolution is formulated by applying a bank of Gabor filters to a blurred image. The key observation is that spatially oriented Gabor filters produce sparse images and that a multichannel version of the observed image can be represented as a product of an unknown nonnegative sparse mixing vector and an unknown nonnegative source image. Therefore a blind-deconv...