Results 1  10
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65
Blind Signal Separation: Statistical Principles
, 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual i ..."
Abstract

Cited by 390 (4 self)
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Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach but requires to venture beyond familiar second order statistics. The objective of this paper is to review some of the approaches that have been recently developed to address this exciting problem, to show how they stem from basic principles and how they relate to each other.
Multichannel Blind Deconvolution: Fir Matrix Algebra And Separation Of Multipath Mixtures
, 1996
"... A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and mat ..."
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Cited by 74 (0 self)
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A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and matrix algorithms for use in multichannel /multipath problems. Using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework. Techniques developed for equalization may be applied to source separation and vice versa. Potential applications of these results lie in neural networks with feedforward memory connections, wideband array processing, and in problems with a multiinput, multioutput network having channels between each source and sensor, such as source separation. Particular applications of FIR polynomial matrix alg...
ICA Using Spacings Estimates of Entropy
 Journal of Machine Learning Research
, 2003
"... This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated KullbackLeibler divergence betwee ..."
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Cited by 46 (3 self)
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This paper presents a new algorithm for the independent components analysis (ICA) problem based on an efficient entropy estimator. Like many previous methods, this algorithm directly minimizes the measure of departure from independence according to the estimated KullbackLeibler divergence between the joint distribution and the product of the marginal distributions. We pair this approach with efficient entropy estimators from the statistics literature. In particular, the entropy estimator we use is consistent and exhibits rapid convergence. The algorithm based on this estimator is simple, computationally efficient, intuitively appealing, and outperforms other well known algorithms. In addition, the estimator's relative insensitivity to outliers translates into superior performance by our ICA algorithm on outlier tests. We present favorable comparisons to the Kernel ICA, FASTICA, JADE, and extended Infomax algorithms in extensive simulations. We also provide public domain source code for our algorithms.
Adaptive blind signal processingneural network approaches
 Proc. of the IEEE
, 1998
"... Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learnin ..."
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Cited by 43 (3 self)
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Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time.
Recent Developments in Blind Channel Equalization: From Cyclostationarity to Subspaces
 Signal Processing
, 1996
"... Since Tong, Xu and Kailath [1] demonstrated the feasibility of identifying possibly nonminimum phase channels using secondorder statistics, considerable research activity, both in algorithm development and fundamental analysis, has been seen in the area of blind identification of multiple FIR chan ..."
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Cited by 35 (1 self)
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Since Tong, Xu and Kailath [1] demonstrated the feasibility of identifying possibly nonminimum phase channels using secondorder statistics, considerable research activity, both in algorithm development and fundamental analysis, has been seen in the area of blind identification of multiple FIR channels. Many of the recently developed approaches invoke, either explicitly or implicitly, the algebraic structure of the data model, while some others resort to the use of cyclic correlation/spectral fitting techniques. The objective of this paper is to establish insightful connections among these studies and present recent developments of blind channel equalization. We also unify various representative algorithms into a common theoretical framework. 1 1
OnLine Blind Multichannel Equalization Based on Mutually Referenced Filters
 IEEE TRANS. SIGNAL PROCESSING
, 1997
"... This paper presents a novel approach to the blind linear equalization of possibly nonminimum phase and timevarying communication channels. In the context of channel diversity, we introduce the concept of mutually referenced equalizers (MRE's) in which several filters are considered, the outputs of w ..."
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Cited by 32 (3 self)
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This paper presents a novel approach to the blind linear equalization of possibly nonminimum phase and timevarying communication channels. In the context of channel diversity, we introduce the concept of mutually referenced equalizers (MRE's) in which several filters are considered, the outputs of which act as training signals for each other. A corresponding (constrained) multidimensional meansquare error (MSE) cost function is derived, the minimization of which is shown to be a necessary and sufficient condition for equalization. The links with a standard linear prediction problem are demonstrated. The proposed technique exhibits properties of important practical concern: 1) The proposed algorithm is globally convergent. 2)
Blind System Identification
, 1997
"... Blind system identification is a fundamental signal processing technology aimed to retrieve unknown information of a system from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation and blind image restoration. Th ..."
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Cited by 22 (1 self)
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Blind system identification is a fundamental signal processing technology aimed to retrieve unknown information of a system from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation and blind image restoration. This paper reviews a number of recently developed concepts and techniques for blind system identification which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input. Keywords: System identification, Blind techniques, Multichannels, Equalization, Source separation. This work has been supported by the Australian Research Council and the Australian Cooperative Research Center for Sensor Signal and Information Processing. y Currently with Motorola Australian Research Centre, 12 Lord Street, Botany 2019, ...
Dynamics Limited Precoding, Shaping, and Blind Equalization for Fast Digital Transmission over Twisted Pair Lines
 IEEE Journal on Selected Areas in Communications
, 1995
"... A new combined precoding/shaping technique for fast digital transmission over twisted pair lines is proposed. Major advantages of this "dynamics shaping" are: Dynamics of the signal at the input of the decision device are reduced by a great amount. Thereby, A/Dconversion, adaptive equalization, and ..."
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Cited by 17 (14 self)
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A new combined precoding/shaping technique for fast digital transmission over twisted pair lines is proposed. Major advantages of this "dynamics shaping" are: Dynamics of the signal at the input of the decision device are reduced by a great amount. Thereby, A/Dconversion, adaptive equalization, and symbol timing are rather facilitated. A tradeoff between signal dynamics at the transmitter output, decision device input and SNRgain by noise whitening is offered. For dynamics limitation relevant in practice, gains up to 6 dB are achieved. Additionally, the transmitter can be fixed to a typical application because, in contrast to TomlinsonHarashima or other precoding techniques, blind adaptive equalization is practicable to remove residual intersymbol interference in the case of a mismatch of precoding and actual cable characteristics. The residual SNRloss is negligible in most applications. SNRgains due to noise prediction, channel coding and signal shaping simply can be combined us...
Blind Sequence Estimation
 IEEE Trans. Communications
, 1995
"... Estimating the data sequence from the received g,gnal without knowing the transmission channel is referred to as blind sequence estimation. A new blind sequence estimation scheme is proposed by exploiting the second order statistical properties of the source and the algebraic structures of the data ..."
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Cited by 14 (0 self)
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Estimating the data sequence from the received g,gnal without knowing the transmission channel is referred to as blind sequence estimation. A new blind sequence estimation scheme is proposed by exploiting the second order statistical properties of the source and the algebraic structures of the data sequence. An optimal source (deterministic) correlation estimator and the Viterbi algorithm are used to achieve blind sequence estimation. The proposed approach also has a special structure particularly attractive for time varying channels.
Adaptive blind source separation and equalization for multipleinput/multipleoutput systems
 IEEE Transactions on Information Theory
"... Abstract—In this paper, we investigate adaptive blind source separation and equalization for multipleinput/multipleoutput (MIMO) systems. We first analyze the convergence of the constant modulus algorithm (CMA) used in MIMO systems (MIMOCMA). Our analysis reveals that the MIMOCMA equalizer is ab ..."
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Cited by 12 (2 self)
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Abstract—In this paper, we investigate adaptive blind source separation and equalization for multipleinput/multipleoutput (MIMO) systems. We first analyze the convergence of the constant modulus algorithm (CMA) used in MIMO systems (MIMOCMA). Our analysis reveals that the MIMOCMA equalizer is able to recover one of the input signals, remove the intersymbol interference (ISI), and suppress the other input signals. Furthermore, for the MIMO finite impulse response (FIR) systems satisfying certain conditions, the MIMOCMA FIR equalizers are able to perfectly recover one of the system inputs regardless of the initial settings. We then propose a novel algorithm for blind source separation and equalization for MIMO systems. Our theoretical analysis proves that the new blind algorithm is able to recover all system inputs simultaneously regardless of the initial settings. Finally, computer simulation examples are presented to confirm our analysis and illustrate the effectiveness of blind source separation and equalization for MIMO systems. Index Terms—Blind equalization, convergence, multipleinput/ multipleoutput system, source separation. I.