Modulation classification plays an important role in uncooperative communication.. In communication intelligence (COMINT) applications the main objective is the perfect monitoring of the intercepted signals and one of the parameters that affect the perfect monitoring is the modulation type of the intercepted signals. In this paper, we derive a kurtosis function-based algorithm for classifying digital modulation signals buried in additive white Gaussian noise. We derive the instantaneous parameter (amplitude, frequency and phase) of received digital modulation signals first, and then the kurtosis used in the identification algorithm are calculated. Computer simulations for different types of digitally modulated signals have been carried out. Results show that all digital modulation types have been classified with success rate≥95% at SNR=8dB.
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