Higher order statistics classification of multi-user chirp modulation signals using clustering techniques

Digital signal classification using clustering has many applications in the civilian and military domains. Most of the proposed classifiers can only recognize a few types of digital signals. This paper presents a novel technique that deals with the classification of multi-user chirp modulation signals using clustering techniques. In this technique, a combination of higher order moments and cumulants are proposed as the effective features. Simulation results show that the proposed technique is able to classify the different types of chirp signals in additive white Gaussian noise (AWGN) channels and fuzzy c-means clustering (FCM) outperform fuzzy k-means clustering (FKM).