Sequential Extraction Algorithm for BSS Without Error Accumulation

Blind source separation (BSS) is an emerging research field in both theory and applications. In this paper we propose a kurtosis maximization algorithm–Sequential Extraction Algorithm, which can extract the source signals sequentially. This approach is based on an algorithm for separating one signal (Algorithm 1) and some technique to eliminate the accumulating errors, which often occur in the sequential extraction steps. In Algorithm 1, a new criterion to judge whether the separated signal is an original source signal, is proposed. In Sequential Extraction Algorithm, we propose a new approach to eliminate accumulating errors, which is caused in the sequential extraction process. This approach is based on the cost function involved in this algorithm, and thus, is different from those available in literature.

[1]  S. Amari,et al.  Gradient adaptation under unit-norm constraints , 1998, Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381).

[2]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[3]  Zhi Ding,et al.  Stationary points of a kurtosis maximization algorithm for blind signal separation and antenna beamforming , 2000, IEEE Trans. Signal Process..

[4]  Jean-Francois Cardoso,et al.  Source separation using higher order moments , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[5]  Petre Stoica,et al.  Maximum likelihood parameter and rank estimation in reduced-rank multivariate linear regressions , 1996, IEEE Trans. Signal Process..

[6]  Andrzej Cichocki,et al.  Nonholonomic Orthogonal Learning Algorithms for Blind Source Separation , 2000, Neural Computation.

[7]  Andrzej Cichocki,et al.  Neural networks for blind decorrelation of signals , 1997, IEEE Trans. Signal Process..

[8]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[9]  Ruey-Wen Liu,et al.  General approach to blind source separation , 1996, IEEE Trans. Signal Process..

[10]  Sun-Yuan Kung,et al.  Extraction of independent components from hybrid mixture: KuicNet learning algorithm and applications , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[11]  Vwani P. Roychowdhury,et al.  Independent component analysis based on nonparametric density estimation , 2004, IEEE Transactions on Neural Networks.

[12]  Sun-Yuan Kung,et al.  Principal Component Neural Networks: Theory and Applications , 1996 .

[13]  Qin Lin,et al.  A unified algorithm for principal and minor components extraction , 1998, Neural Networks.

[14]  Dinh-Tuan Pham,et al.  Blind separation of instantaneous mixture of sources based on order statistics , 2000, IEEE Trans. Signal Process..

[15]  R. Liu,et al.  AMUSE: a new blind identification algorithm , 1990, IEEE International Symposium on Circuits and Systems.

[16]  Ehud Weinstein,et al.  New criteria for blind deconvolution of nonminimum phase systems (channels) , 1990, IEEE Trans. Inf. Theory.

[17]  Andrzej Cichocki,et al.  Stability Analysis of Learning Algorithms for Blind Source Separation , 1997, Neural Networks.