The idea that common blind techniques based on Independent Component Analysis (ICA) behave in noisy environment like a biased MMSE separator (sometimes called Maximum Ratio Combiner (MRC)) was introduced in our recent work [3]. In this paper, we put this in more precise terms by doing an analysis of the bias of approaches that are based on known ICA algorithm FastICA. We show that the one-unit ...

Independent Component Analysis (ICA) technique separates mixed signals blindly without any information of mixing system. The present work studies and analyses the issues involved in interference rejection in direct sequence spread spectrum communication systems based on Independent Component Analysis technique. The ICA technique tries to separate the unwanted interfering signal from the desired...

Independent component analysis (ICA) is an important unsupervised learning method. Most popular ICAmethods use kurtosis as a metric of non-Gaussianity to maximize, such as FastICA and JADE. However, their assumption of kurtosic sources may not always be satisfied in practice. For weak-kurtosic but skewed sources, kurtosis-based methods could fail while skewness-based methods seem more promising...

This paper presents a new algorithm for the independent components analysis (ICA) problem based on efficient spacings estimates of entropy. Like many previous methods, we minimize a standard measure of the departure from independence, the estimated Kullback-Leibler divergence between a joint distribution and the product of its marginals. To do this, we use a consistent and rapidly converging en...

— Aiming at the problem of blind estimation of Pseudo-random (PN) sequences in multi-user long scrambling code direct sequence spread spectrum (LSC-DSSS) signals, in this paper, we proposed a novel PN sequences estimation method based on Fast-ICA algorithm and third-order statistics. The received signal is firstly divided twice into segments and then the segments are transformed into several a...

In several applications, such as wideband spectrum sensing for cognitive radio, only the power spectrum (a.k.a. the power spectral density) is of interest and there is no need to recover the original signal itself. In addition, high-rate analog-to-digital converters (ADCs) are too power hungry for direct wideband spectrum sensing. These two facts have motivated us to investigate compressive wid...

The use of Blind Signal Separation methods (ICA and other approaches) for the analysis of astrophysical data remains quite unex-plored. In this paper, we present a new approach for analyzing the in-frared emission spectra of interstellar dust, obtained with NASA's Spitzer Space Telescope, using FastICA and Non-negative Matrix Factorization (NMF). Using these two methods, we were able to unveil ...

Independent component analysis (ICA) solves the blind source separation problem by evaluating higher-order statistics, e.g. by estimating fourthorder moments. While estimation errors of the kurtosis can be shown to asymptotically decay with sample size according to a square-root law, they are subject to two further effects for finite samples. Firstly, errors in the estimation of kurtosis increa...

Recently, the authors developed the Minimax Mutual Information algorithm for linear ICA of real-valued mixtures, which is based on a density estimate stemming from Jaynes’ maximum entropy principle. Since the entropy estimates result in an approximate upper bound for the actual mutual information of the separated outputs, minimizing this upper bound results in a robust performance and good gene...