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| A CLASS OF APPROACHES FOR BLIND SOURCE SEPARATION BASED ON MULTIVARIATE DENSITY ESTIMATION |
| He Zhenya①; Yang Luxi①; Liu Ju①; Lu Ziyi①; He Chen② |
| ①Radio Eng. Dept., Southeast University Nanjing 210096 China;②Electronic Eng. Dept., Shanghai Jiaotong University Shanghai 200030 China |
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Abstract A class of learning algorithms is drived for blind separation of independent source signals in this paper. These algorithms are based on minimizing a contrast function defined in terms of the Kullback-Leibler distance. By utilizing the technique of multivariate density esti-mation, two types of separating algorithms are obtained. Simulations illustrate the effectiveness of the algorithms.
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Received: 14 April 1999
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2001, Vol. 23 
