Since the beginning of the last two decades, many researchers have been involved in the problem of Blind Source Separation (BSS). Whilst hundreds of algorithms have been proposed to solve BSS. These algorithms are well known as Independent Component Analysis (ICA) algorithms. Nowadays, ICA algorithms have been used to deal with various applications and they are using many performance indices. This paper is dedicated to classify the different algorithms according to their applications and performances.

The present paper deals with the blind deconvolution of a Multiple-Input Multiple-Output Finite Impulse Response (MIMO-FIR) system. To deal with the blind deconvolution problem using the second-order statistics (SOS) of the outputs, Hua and Tugnait considered it under the conditions that a) the FIR system is irreducible and b) the input signals are spatially uncorrelated and have distinct power spectra. In the present paper, the problem is considered under a weaker condition than the condition a). Namely, we assume that c) the FIR system is equalizable by means of the SOS of the outputs. Under b) and c), we show that the system can be blindly identified up to a permutation, a scaling, and a delay using the SOS of the outputs. Moreover, based on this identifiability, we show a novel necessary and sufficiently condition for solving the blind deconvolution problem, and then, based on the condition, we propose a new algorithm for finding an equalizer using the SOS of the outputs, while Hua and Tugnait have not proposed any algorithm for solving the blind deconvolution under the conditions a) and b).

This paper proposes a new method for recovering the original signals from their linear mixtures observed by the same number of sensors. It is performed by identifying the linear transform from the sources to the sensors, only using the sensor signals. The only assumption of the source signals is basically the fact that they are statistically mutually independent. In order to perform the 'blind' identification, some time-correlational information in the observed signals are utilized. The most important feature of the method is that the full information of available time-correlation data (second-order statistics) is evaluated, as opposed to the conventional methods. To this end, an information-theoretic cost function is introduced, and the unknown linear transform is found by minimizing it. The propsed method gives a more stable solution than the conventional methods.

The present letter deals with the blind equalization problem of a single-input single-output infinite impulse response (SISO-IIR) channel with additive Gaussian noise. To solve the problem, we propose a new criterion for maximizing constrainedly a fourth-order cumulant. The algorithms derived from the criterion have such a novel property that even if Gaussian noise is added to the output of the channel, an effective zero-forcing (ZF) equalizer can be obtained with as little influence of Gaussian noise as possible. To show the validity of the proposed criterion, some simulation results are presented.