Abstract
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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Sahmoudi, M., Abed-Meraim, K., Benidir, M. (2004). Blind Separation of Heavy-Tailed Signals Using Normalized Statistics. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_15
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DOI: https://doi.org/10.1007/978-3-540-30110-3_15
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