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Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation
Elk, Poland June 28-June 30
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CISIM.2007.622007 6th International Conference on ...
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Yuri Grishin, "Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation," Computer Information Systems and Industrial Management Applications, International Conference on, pp. 197-202, 2007 6th International Conference on Computer Information Systems and Industrial Management Applications, 2007.
 
 
BibTex
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@article{ 10.1109/CISIM.2007.62,
author = {Yuri Grishin},
title = {Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation},
journal ={Computer Information Systems and Industrial Management Applications, International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2894-5},
pages = {197-202},
doi = {http://doi.ieeecomputersociety.org/10.1109/CISIM.2007.62},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Information Systems and Industrial Management Applications, International Conference on
TI - Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation
SN - 0-7695-2894-5
SP197
EP202
A1 - Yuri Grishin,
PY - 2007
KW - null
VL - 0
JA - Computer Information Systems and Industrial Management Applications, International Conference on
ER -
 
Yuri Grishin, Bialystok Technical University, Poland
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.
Citation:
Yuri Grishin, "Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation," cisim, pp.197-202, 2007 6th International Conference on Computer Information Systems and Industrial Management Applications, 2007
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