This paper introduces a new approach for the blind separation (BS) of heavy tailed signals that can be modeled by real-valued symmetric α-stable (SαS) processes. As the second and higher order moments of the latter are infinite, we propose to use normalized statistics of the observation to achieve the BS of the sources. More precisely, we show that the considered normalized statistics are convergent (i.e., take finite values) and have the appropriate structure that allows for the use of standard BS techniques based on second and higher order cumulants. © Springer-Verlag 2004.
CITATION STYLE
Sahmoudi, M., Abed-Meraim, K., & Benidir, M. (2004). Blind separation of heavy-tailed signals using normalized statistics. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 113–120. https://doi.org/10.1007/978-3-540-30110-3_15
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