Recognition and Classification of Direct Spread Spectrum Signals

This paper deals with the design and implementation of a direct spread spectrum signal classifier. The Binary-Phase Shift Keying (BPSK) and Quadrature Phase-Shift Keying (QPSK) modulation were used to create the signal patterns and Pseudo-Random (PR) sequences, Gold codes and Kasami codes were used to directly spread the spectrum of the modulated signals. The properties of the proposed direct spread spectrum signal models were simulated for Signal-to-Noise Ratio (SNR) from 0 to 10 dB for different spread factor to make their properties close to real direct spread spectrum signals. The Welch’s power spectral density estimation method, wavelet transform and signal autocorrelation detection method were used for conceptual design and implementation of the direct spread spectrum signal classifier in Matlab programming environment.