This letter investigates an improved blind source separation algorithm based on Maximum Entropy （ME） criteria. The original ME algorithm chooses the fixed exponential or sigmoid ftmction as the nonlinear mapping fun...This letter investigates an improved blind source separation algorithm based on Maximum Entropy （ME） criteria. The original ME algorithm chooses the fixed exponential or sigmoid ftmction as the nonlinear mapping function which can not match the original signal very well. A parameter estimation method is employed in this letter to approach the probability of density function of any signal with parameter-steered generalized exponential function. An improved learning rule and a natural gradient update formula of unmixing matrix are also presented. The algorithm of this letter can separate the mixture of super-Gaussian signals and also the mixture of sub-Gaussian signals. The simulation experiment demonstrates the efficiency of the algorithm.展开更多
An on-line blind source separation （BSS） algorithm is presented in this paper under the assumption that sources are temporarily correlated signals. By using only some of the observed samples in a recursive calculati...An on-line blind source separation （BSS） algorithm is presented in this paper under the assumption that sources are temporarily correlated signals. By using only some of the observed samples in a recursive calculation, the whitening matrix and the rotation matrix could be approximately obtained through the measurement of only one cost function. SimNations show goad performance of the algorithm.展开更多
This paper presents a new blind separation approach of the low order cyclostationary signals based on the cyclic periodicity of the cyclostationary signal.The goal of the method is extracting the hidden periodicity an...This paper presents a new blind separation approach of the low order cyclostationary signals based on the cyclic periodicity of the cyclostationary signal.The goal of the method is extracting the hidden periodicity and reducing the randomicity of cyclostationary signal and it is particularly applicable to the separation of low order cyclostationary signals.The method also demonstrates the importance of extraction of cyclostationary signals from low order to high order in turn.The effectiveness of the proposed method is finally demonstrated by computer simulation and experiment.展开更多
The contrast function remains to be an open problem in blind source separation （BSS） when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual...The contrast function remains to be an open problem in blind source separation （BSS） when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank. The mutual information reaches its minimum at the separation points, where the random outputs of the BSS system are the scaled and permuted source signals, while the others are zero outputs. Using the property that the transpose of the mixing matrix and a matrix composed by m observed signals have the indentical null space with probability one, a practical method, which can detect the unknown number of source signals n, ulteriorly traces the dynamical change of the sources number with a few of data, is proposed. The effectiveness of the proposed theorey and the developed novel algorithm is verified by adaptive BSS simulations with unknown and dynamically changing number of source signals.展开更多
文摘This letter investigates an improved blind source separation algorithm based on Maximum Entropy （ME） criteria. The original ME algorithm chooses the fixed exponential or sigmoid ftmction as the nonlinear mapping function which can not match the original signal very well. A parameter estimation method is employed in this letter to approach the probability of density function of any signal with parameter-steered generalized exponential function. An improved learning rule and a natural gradient update formula of unmixing matrix are also presented. The algorithm of this letter can separate the mixture of super-Gaussian signals and also the mixture of sub-Gaussian signals. The simulation experiment demonstrates the efficiency of the algorithm.
基金This project was supported by the National 863 project (2001AA422420 -02)
文摘An on-line blind source separation （BSS） algorithm is presented in this paper under the assumption that sources are temporarily correlated signals. By using only some of the observed samples in a recursive calculation, the whitening matrix and the rotation matrix could be approximately obtained through the measurement of only one cost function. SimNations show goad performance of the algorithm.
基金Doctor Foundations of Henan polytechnic university(648391)NSFC(U1304523,51205371)
文摘This paper presents a new blind separation approach of the low order cyclostationary signals based on the cyclic periodicity of the cyclostationary signal.The goal of the method is extracting the hidden periodicity and reducing the randomicity of cyclostationary signal and it is particularly applicable to the separation of low order cyclostationary signals.The method also demonstrates the importance of extraction of cyclostationary signals from low order to high order in turn.The effectiveness of the proposed method is finally demonstrated by computer simulation and experiment.
基金supported by the National Natural Science Foundation of China(Grant No.60496311).
文摘The contrast function remains to be an open problem in blind source separation （BSS） when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank. The mutual information reaches its minimum at the separation points, where the random outputs of the BSS system are the scaled and permuted source signals, while the others are zero outputs. Using the property that the transpose of the mixing matrix and a matrix composed by m observed signals have the indentical null space with probability one, a practical method, which can detect the unknown number of source signals n, ulteriorly traces the dynamical change of the sources number with a few of data, is proposed. The effectiveness of the proposed theorey and the developed novel algorithm is verified by adaptive BSS simulations with unknown and dynamically changing number of source signals.
