| Paper: | MLSP-P1.6 | ||
| Session: | Blind Source Separation II | ||
| Time: | Tuesday, May 16, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | Robust Super-Exponential Methods for Blind Equalization of MIMO-IIR Systems | ||
| Authors: | Kiyotaka Kohno, Yujiro Inouye, Shimane University, Japan; Mitsuru Kawamoto, National Institute of Advanced Industrial Science and Technology, Japan | ||
| Abstract: | The so called "super-exponential" methods (SEMs) are attractive methods for solving multichannel blind deconvolution problem. The conventional SEMs, however, have such a drawback that they are very sensitive to Gaussian noise. To overcome this drawback, the robust super-exponential method (RSEM) were proposed for single-input single-output infinite impulse response (SISO-IIR) channels and for multi-input multi-output (MIMO) static channels (instantaneous mixtures). While the conventional SEMs use the second- and higher-order cumulants of observations, the RSEM uses only the higher-order cumulants of observations. Since higher-order cumulants are insensitive to Gaussian noise, the RSEM is robust to Gaussian noise. We proposed an RSEM extended to the case of MIMO-IIR channels (convolutive mixtures). To show the validity of the proposed RSEM, some simulation results are presented. | ||
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