Classifying emitters in the high frequency range with self-organizing maps

被引：0

作者：

Fanghänel, K ^{[1
]}

Köllmann, K ^{[1
]}

Raps, F ^{[1
]}

Zeidler, HC ^{[1
]}

机构：

[1] Univ Bundeswehr Hamburg, Holstenhofweg 85, D-22043 Hamburg, Germany

来源：

IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI | 2000年

关键词：

D O I：

10.1109/IJCNN.2000.859407

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：265 / 269

页数：5

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