Abstract
In this paper a data-driven approach for signal separation over the digital domain is discussed. The proposed approach solves the problem as a classification task and it is widely experimented over electromagnetic signals in open scenarios. Results show that high levels of accuracy are reachable through a relatively easy learning method over simulated data.
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Filice, S., Croce, D., Basili, R. (2013). A Robust Machine Learning Approach for Signal Separation and Classification. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_89
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DOI: https://doi.org/10.1007/978-3-642-38628-2_89
Publisher Name: Springer, Berlin, Heidelberg
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