Automatic Modulation Recognition for Secondary Modulated Signals

Automatic modulation recognition (AMR) for secondary modulated signals, remains as a bottleneck, since both analog and digital modulation are adopted in one signal. To address this issue, we focus on the original signal spectrum and the 2nd order spectrum of the pre-demodulated signal, which imply modulation features. Based on this idea, a novel statistic for discrimination is proposed and the feature vector is formed. Finally, the support vector machine (SVM) is adopted to implement the classification work.

Automated summary

The paper focuses on the original signal spectrum and the second-order spectrum of the pre-demodulated signal to extract modulation features, proposes a novel statistic for discrimination, and uses support vector machine (SVM) for classification.