Multichannel blind source separation using convolution kernel compensation
Multichannel blind source separation using convolution kernel compensation
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DOI：
10.1109/tsp.2007.896108
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发表时间：
2007-09-01
影响因子：
5.4
通讯作者：
Zazula, Damjan
中科院分区：
文献类型：
--
作者：
Holobar, Ales;Zazula, Damjan
本文研究了一种新的分解技术，适用于由有限长符号组成的线性混合信号的盲分离。首先将观测到的符号建模为多输入多输出（MIMO）模型中的信道响应，而信道输入在概念上被认为是携带关于符号出现时间的信息的稀疏正脉冲序列。我们的分解方法补偿信道响应，旨在直接重建输入脉冲序列。该算法首先推导出超定无噪声MIMO的情况下。然后，在噪声环境中的欠定混合提供了一个广义的计划。虽然盲目的，所提出的技术方法贝叶斯最优线性最小均方误差估计，因此，显着的抗噪性。仿真实验结果表明，该方法不仅适用于欠定卷积混合，而且适用于互相关最大可达10%的中等相关输入脉冲序列的混合。
This paper studies a novel decomposition technique, suitable for blind separation of linear mixtures of signals comprising finite-length symbols. The observed symbols are first modeled as channel responses in a multiple-input-multiple-output (MIMO) model, while the channel inputs are conceptually considered sparse positive pulse trains carrying the information about the symbol arising times. Our decomposition approach compensates channel responses and aims at reconstructing the input pulse trains directly. The algorithm is derived first for the overdetermined noiseless MIMO case. A generalized scheme is then provided for the underdetermined mixtures in noisy environments. Although blind, the proposed technique approaches Bayesian optimal linear minimum mean square error estimator and is, hence, significantly noise resistant. The results of simulation tests prove it can be applied to considerably underdetermined convolutive mixtures and even to the mixtures of moderately correlated input pulse trains, with their cross-correlation up to 10% of its maximum possible value.