A new blind deconvolution algorithm for SIMO channel based on neural network

This paper presents a novel approach for blind deconvolution of SIMO channel, and a MISO neural network can easily implement the proposed algorithm. Contrary to gradient-based algorithms, there are no step size parameters to choose. If the number of observed signals is not less than 2, the source signal can usually be separated from the observed mixtures. The new algorithm converges very fast. Furthermore, the extra assumptions proposed in Luengo's and Zhang's algorithms about the source signal are not necessary in this paper. The experiments demonstrate the good performance of our algorithm and show that the proposed algorithm is robust in noise environment.

[1]  Andrzej Cichocki,et al.  Two spatio-temporal decorrelation learning algorithms and their application to multichannel blind deconvolution , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[2]  Jacob Benesty,et al.  Optimal step size of the adaptive multichannel LMS algorithm for blind SIMO identification , 2005, IEEE Signal Processing Letters.

[3]  Christian Jutten,et al.  Blind source separation for convolutive mixtures , 1995, Signal Process..

[4]  Tan Li-l.  Multi-Input Multi-output (MIMO) Blind Deconvolution Via Maximum Entropy (ME) Method , 2000 .

[5]  Adriana Dapena,et al.  A novel frequency domain approach for separating convolutive mixtures of temporally-white signals , 2003, Digit. Signal Process..

[6]  L. Vielva,et al.  A fast blind SIMO channel identification algorithm for sparse sources , 2003, IEEE Signal Processing Letters.

[7]  Kari Torkkola,et al.  Blind separation of delayed sources based on information maximization , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[8]  Lucas C. Parra,et al.  On-line Convolutive Blind Source Separation of Non-Stationary Signals , 2000, J. VLSI Signal Process..

[9]  Te-Won Lee,et al.  Blind Separation of Delayed and Convolved Sources , 1996, NIPS.

[10]  Liu Ju.  A Survey of Blind Source Separation and Blind Deconvolution , 2002 .

[11]  Ehud Weinstein,et al.  Criteria for multichannel signal separation , 1994, IEEE Trans. Signal Process..

[12]  Wang Jun,et al.  A new approach to blind identification of SIMO channels with common zeros , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).