| Blind source separation with unknown and dynamically changing number of source signals |
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| 摘 要： | 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.
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| 收稿时间： | 31 October 2005 |
| 修稿时间： | 8 December 2005 |
Blind source separation with unknown and dynamically changing number of source signals |
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| Authors: | YE Jimin ZHANG Xianda ZHU Xiaolong |
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| Affiliation: | 1. Key Laboratory for Radar Signal Processing, Xidian University, Xi'an 710071, China
2. Department of Automation, State Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China |
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| Abstract: | 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. |
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| Keywords: | blind source separation independent component analysis natural gradient mutual information contrast function neural networks |
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