Elements of information theory
Elements of information theory
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
An analytical constant modulus algorithm
IEEE Transactions on Signal Processing
Blind separation of synchronous co-channel digital signals using anantenna array. I. Algorithms
IEEE Transactions on Signal Processing
Blind Deconvolution of Multi-Input Single-Output Systems With Binary Sources
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources based on orderstatistics
IEEE Transactions on Signal Processing
Asymptotic properties of the algebraic constant modulus algorithm
IEEE Transactions on Signal Processing
A simple geometric blind source separation method for bounded magnitude sources
IEEE Transactions on Signal Processing
Source separation when the input sources are discrete or haveconstant modulus
IEEE Transactions on Signal Processing
Complex random vectors and ICA models: identifiability, uniqueness, and separability
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
A two-stage Independent Component Analysis-based method for blind detection in CDMA systems
Digital Signal Processing
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This study presents a blind and geometric technique which pursues the linear decomposition of the observations in bounded component signals. The bounded component analysis of the observations relies on the hypotheses of compactness and Cartesian decomposition of the convex support of the vector of component signals, and in the invertibility of the mixture. Assumptions, which in absence of noise, are able to guarantee the identifiability of the mixture and separability of the components, up to permutation, scaling, and phase ambiguities. Under these conditions, the convex perimeter of the normalized linear combination of the observations is shown to be a global contrast function whose minima correspond with the extraction of bounded components of the observations. Practical extraction and separation algorithms based on the minimization of this criterion are given. The experimental results with communications signals serve to illustrate the good performance of the proposed method in high SNR scenarios, even for a small number of samples.