A Feedback Approach and Its Learning Algorithm for Over Complete Blind Source Separation
A Feedback Approach and Its Learning Algorithm for Over Complete Blind Source Separation
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DOI：
10.1109/ispacs.2006.364696
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发表时间：
2006-12
期刊：
影响因子：
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通讯作者：
Kenji Nakayama;Haruo Katou;A. Hirano
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文献类型：
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作者：
Kenji Nakayama;Haruo Katou;A. Hirano
在盲源分离（BSS）应用中，信号源的数量是未知的。当传感器的数量少于信号源的数量时，这个问题被称为“过完备BSS”（OC-BSS），这是由于观测信息缺乏而导致的一个难题。本文提出了一种针对 OC-BSS 的反馈方法及其学习算法。混合块的输出的数量被设置为等于传感器的数量。通过假设某种条件，至少一个输出可以分离单个信号源。该输出被反馈到解混块的输入，并从观测值中减去，以减少等效信号源的数量。提出了两种反馈方法。其中一种是直接相减，另一种是根据反馈信号和观测信号的直方图进行样本消除。修改后的观察结果被进一步分离。重复相同的过程，直到所有信号源被分离。通过计算机模拟评估所提出方法的性能。与传统方法相比，所提出的方法可以将信号干扰比提高几个dB
In blind source separation (BSS) applications, the number of the signal sources is not known. When the number of the sensors is less than that of the signal sources, this problem is called over complete BSS' (OC-BSS), which is a difficult problem due to lack of information in observations. In this paper, a feedback approach and its learning algorithm are proposed for the OC-BSS. The number of the outputs of an umixing block is set to be equal to that of the sensors. By assuming some condition, at least one output can separate a single signal source. This output is fed back to the inputs of the unmixing block, and is subtracted from the observations, in order to reduce the number of equivalent signal sources. Two kinds of feedback methods are proposed. One of them is direct subtraction and the other is sample elimination based on histogram of the feedback signal and the observed signals. The modified observations are further separated. The same process is repeated until all signal sources are separated. Performance of the proposed method is evaluated through computer simulation. The proposed method can improve a signal to interference ratio by the several dB compared to the conventional methods