Neural Network for Classification of Multi-User Chirp Modulation Signals using Wavelet Higher Order Statistics Features

Signal classification has many important applications in both of the civilian and military domains. This paper presents a classification of multi-user chirp modulation signals using wavelet higher order statistics features and neural network classifier (NN). In this paper, even higher order moments and cumulants up to order eight from the discrete wavelet transform (DWT) coefficients are proposed as effective features. These features are used for classification of eight multi-user chirp modulation signals using neural network classifier. Simulation results show that the proposed technique is able to classify these eight chirp signals in additive white Gaussian noise (AWGN) channels with high accuracy and the performance using features extracted from wavelet transform outperforms that extracted from the signals themselves. Also the features extracted from only details coefficients outperforms the features extracted from the total wavelet coefficients and from the approximation coefficients only and different decomposition levels for wavelet are used.