On extending the Noisy Independent Component Analysis to Impulsive Components

被引:1
作者
Feng, Pingxing [1 ]
Li, Liping [1 ]
机构
[1] Univ Elect Sci & Technol China, Sch Elect Engn, Chengdu 611731, Peoples R China
关键词
Independent component analysis; Multidimensional signal processing; Impulsive noise; Signal representations; BLIND SEPARATION; MAXIMUM-LIKELIHOOD; MIXTURE; ALGORITHMS; SHRINKAGE; ICA;
D O I
10.1007/s11277-015-3135-2
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
As an important factor in the fast fixed-point algorithm of independent component analysis (ICA), noise has a significant influence on the separate performance of ICA. Unfortunately, the traditional algorithm of noisy ICA did not address the influence of impulsive components. Because the sources were signals mixed with impulsive noise, the Gaussian noisy algorithm will be invalid for separating the sources. In general, those measurements that significantly deviate from the normal pattern of sensed data are considered impulses. In this paper, we introduce a non-linear function based on the S-estimator to identify the impulsive components in the observed data. This approach guarantees that the impulse noise can be detected from the observed signal. Furthermore, a threshold for the impulse components and methods to remove impulse noise and reconstruct the signal is proposed. The proposed technique improves the separate performance of the traditional algorithm for Gaussian noisy ICA. With the proposed method, the fast fixed-point algorithm of ICA is more reliable for noisy situations. The simulation results show the effectiveness of the proposed method.
引用
收藏
页码:415 / 427
页数:13
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