In this work, we propose and analyze a method to solve the problem of underdetermined blind source separation (and identification) that employs the ideas of sparse component analysis (SCA) and independent component analysis (ICA). The main rationale of the approach is to allow the possibility of reaching a method that is more robust with respect to the degree of sparseness of the involved signals and more effective in the use of information brought by multiple sensors. The ICA-based solution is tested with the aid of three representative scenarios and its performance is compared with that of one of the soundest SCA techniques available, the DEMIXN algorithm. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Nadalin, E. Z., Suyama, R., & Attux, R. (2009). An ICA-Based method for blind source separation in sparse domains. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5441, pp. 597–604). https://doi.org/10.1007/978-3-642-00599-2_75
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