Automatic digital modulation classification using instantaneous features

In this paper, we propose a simple, effective and robust method based on the statistical moments of instantaneous features to classify digital modulation signals. This method adopts a tree structure scheme and uses different features in each branch to make full use of distinguishing modulation type´s characteristics. The proposed method is capable of differentiating ASK2, ASK4, FSK2, FSK4, PSK2 and PSK4 signals at the output of typical high frequency channel with white Gaussian noise, multi-path delay and Doppler shift. Unlike most other existing methods, our method assumes no prior information of the incoming signal (symbol rate, carrier frequency, amplitude etc.). Extensive simulation results demonstrate that this approach is robust in various practical situations.