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Spread Spectrum Signals Classification Based on the Wigner-Ville Distribution and Neural Network Probability Density Function Estimation

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1 Author(s)
Grishin, Y. ; Bialystok Tech. Univ., Bialystok

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.

Published in:

Computer Information Systems and Industrial Management Applications, 2007. CISIM '07. 6th International Conference on

Date of Conference:

28-30 June 2007