Distinguishing BFSK from QAM and PSK by sampling once per symbol

In this paper we propose a feature to distinguish FSK from QAM and PSK modulations. The feature is based on the imaginary part of product of two consecutive signal values where every symbol is sampled only once. Conditional probability density functions of the feature given the present modulation are determined. Central limit theorem for strictly stationary m-dependent sequences is used to obtain Gaussian approximations. Then the thresholds are determined based on the minimization of total probability of misclassification. Effects of AWGN, carrier offset and non-synchronized sampling on the performance are studied. Proposed classifier is compared to the maximum likelihood classifier.