Classifying Emitters in the High Frequency Range with Self-Organizing Maps

I
IJCNN
2000
IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6
Abstract - Classifying Emitters in the High Frequency Range with Self-Organizing Maps

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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6

Como, Italy

July 24-July 27

ISBN: 0-7695-0619-4

	ASCII Text	x
	Karsten Fanghänel, Kuno Köllmann, Frank Raps, Hans Christoph Zeidler, "Classifying Emitters in the High Frequency Range with Self-Organizing Maps," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 6, pp. 6265, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.859407, author = {Karsten Fanghänel and Kuno Köllmann and Frank Raps and Hans Christoph Zeidler}, title = {Classifying Emitters in the High Frequency Range with Self-Organizing Maps}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {6}, year = {2000}, issn = {1098-7576}, pages = {6265}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859407}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - Classifying Emitters in the High Frequency Range with Self-Organizing Maps SN - 1098-7576 SP EP A1 - Karsten Fanghänel, A1 - Kuno Köllmann, A1 - Frank Raps, A1 - Hans Christoph Zeidler, PY - 2000 VL - 6 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

Karsten Fanghänel, Universit?t der Bundeswehr Hamburg

Kuno Köllmann, Universit?t der Bundeswehr Hamburg

Frank Raps, Universit?t der Bundeswehr Hamburg

Hans Christoph Zeidler, Universit?t der Bundeswehr Hamburg

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859407

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.

Citation:

Karsten Fanghänel, Kuno Köllmann, Frank Raps, Hans Christoph Zeidler, "Classifying Emitters in the High Frequency Range with Self-Organizing Maps," ijcnn, vol. 6, pp.6265, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000

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