Classification of multi-user chirp modulation signals using higher order cumulant features and four types of classifiers

Automatic Digital signal type classification (ADSTC) has many important applications in both of 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. In this technique, a combination of higher order moments and higher order cumulants (up to eighth) are proposed as the effective features and different types of classifiers are used. Simulation results show that the proposed technique is able to classify the different types of chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy and the neural network classifier (NN) outperforms other classifiers, namely, maximum likelihood classifier (ML), k nearest neighbor classifier (KNN), support vector machine classifier (SVM).