Adaptive Robust Super-exponential Algorithms for Deflationary Blind Equalization of Instantaneous Mixtures View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2004

AUTHORS

Masanori Ito , Masashi Ohata , Mitsuru Kawamoto , Toshiharu Mukai , Yujiro Inouye , Noboru Ohnishi

ABSTRACT

The so called “super-exponential” algorithms (SEA’s) are attractive algorithms for solving blind signal processing problems. The conventional SEA’s, however, have such a drawback that they are very sensitive to Gaussian noise. To overcome this drawback, we propose a new SEA. While the conventional SEA’s use the second- and higher-order cumulants of observations, the proposed SEA uses only the higher-order cumulants of observations. Since higher-order cumulants are insensitive to Gaussian noise, the proposed SEA is robust to Gaussian noise, which is referred to as a robust super-exponential algorithm (RSEA). The proposed RSEA is implemented as an adaptive algorithm, which is referred to as an adaptive robust super-exponential algorithm (ARSEA). To show the validity of the ARSEA, some simulation results are presented. More... »

PAGES

374-381

Book

TITLE

Independent Component Analysis and Blind Signal Separation

ISBN

978-3-540-23056-4
978-3-540-30110-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-30110-3_48

DOI

http://dx.doi.org/10.1007/978-3-540-30110-3_48

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001010176


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