Spread spectrum signals classification based on the Wigner-Ville distribution and neural network probability density function estimation

被引：0
|
作者：
Grishin, Yuri [1 ]
机构：
[1] Bialystok Tech Univ, Fac Elect Engn, Wiejska 45D, PL-15351 Bialystok, Poland
来源：
6TH INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL MANAGEMENT APPLICATIONS, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/CISIM.2007.62
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
A spread spectrum signal recognition can be accomplished by exploiting the particular features of modulation presented in a received signal observed in presence of noise. These modulation features are the result of slight transmitter component variations and acts as an individual signature of a transmitter. The paper describes a spread spectrum signal classification algorithm based on using the Wigner-Ville Distribution (WVD), noise reduction procedure with using a two-dimensional filter and the RBF neural network probability density function estimator which extracts the features vector used for the final signal classification. The numerical simulation results for the P4-coded signals are presented.
引用
收藏
页码：197 / +
页数：2
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