Classification of modulation of signals of interest

In this paper, we present a novel algorithm to automatically characterize and classify the modulation of signals of interest (SOI). To uniquely characterize SOIs that are closely related, we have derived a set of robust features that are based on information theoretic measures such as Renyi entropy and relative entropy and high order statistics. Two measures based on mutual information and relative entropy are also developed to assess the value of a feature - a mutual-information based measure is used to select non-redundant features and a relative entropy based measure is used to select a feature that would improve the classification accuracy. We have developed a multi-class classifier that is constructed by combining a set of binary support vector machines (SVMs). Our experimental results show that the features that we have considered can characterize the modulation of different closely related SOIs very well, and that our modulation classifier is efficient and effective in classifying the modulation of a signal with an average accuracy of 99.5% at SNR of 10 dB. The techniques developed in this paper can be used to classify the modulation of communication signals.