A novel Automatic Modulation Classification method based on Stockwell-transform and energy entropy for underwater acoustic signals

Abstract

Automatic Modulation Classification (AMC) of communication signals plays a significant role in communication systems. However, conventional methods of modulation classification have poor performance in a shallow water environment. Recently, the Stockwell-transform (S-transform), a new time-frequency analysis method, receives widely attention in different areas. In this paper, we introduce the S-transform into modulation classification and propose a novel method of modulation classification under underwater acoustic channel. Firstly, we set up a model of underwater acoustic channel based on Bellhop and the multipath Rayleigh fading channel model. Next, we extract features of energy entropy of S-transform time-frequency spectrum of signals, and then input them into the classifier, Support Vector Machine (SVM). Meanwhile, different signal sets are considered, which have different number of signal schemes. Finally, Matlab simulating experiments are performed to evaluate the performance of the proposed method for each signal set under AWGN channel, and results show that the proposed method reaches higher probability of correct classification than convention methods. Aiming at the problem under multipath fading channel, especially underwater acoustic channel, the simulated results show it effectiveness.