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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6   p. 6265
Classifying Emitters in the High Frequency Range with Self-Organizing Maps
Karsten Fanghanel, Universität der Bundeswehr Hamburg
Kuno Kollmann, Universität der Bundeswehr Hamburg
Frank Raps, Universität der Bundeswehr Hamburg
Hans Christoph Zeidler, Universität der Bundeswehr Hamburg

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859407
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Abstract
In this paper, Self-Organizing Maps (SOMs) are proposed for classifying emitters in the high frequency range allowing verification of emitters received by dislocated sensors. With respect to the characteristics of SOMs the classification and verification can be done without any model based knowledge of the different transmission channels. Moreover, both processes seem to be robust against data losses based on a discrete wavelet transform.
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Citation:  Karsten Fanghanel, Kuno Kollmann, Frank Raps, Hans Christoph Zeidler, "Classifying Emitters in the High Frequency Range with Self-Organizing Maps," ijcnn, p. 6265,  IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6,  2000