Fold-based Kolmogorov–Smirnov Modulation Classifier

Abstract

Modulation classification is crucial in applications such as electronic warfare and interference cancellation. In this letter, a novel feature-based Kolmogorov-Smirnov classifier is proposed for the identification of the modulation formats. The received signal is first preprocessed with a folding operation that helps identify the modulation formats based on their different axes of symmetry. Simulation results show that the performance of the proposed classifier is close to that of the optimal likelihood-based classifier, while its robustness to noise uncertainty is improved and its computational complexity is reduced compared to that of the optimal likelihood-based classifier.