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

页码：197 / +

页数：2

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