Abstract
A method for automatic classification of composite FM/PM speech signals is proposed here. The method classifies a number of intercepted signals into FM or PM, provided that each signal is either speech FM or PM. The signals are transmitted through flat fading channels and contaminated with white Gaussian noise. A linear array consisting of several sensors is constructed to intercept these signal. The minimum description length criterion is used to estimate the number of intercepted signals. The maximum likelihood approach is used to estimate the direction of arrival of each signal, the fading channel, coefficients and the noise variance. The classification procedure is based on estimation of the phase waveform which contains the speech signal. An iterative algorithm is derived to perform this estimate for each signal and then the resulting estimate is used to develop a method to determine, for each signal, if it is FM or PM. The method is based on evaluating the power spectral density of the instantaneous frequency of the intercepted signal which is distinctive in speech FM and PM signals. Computer simulations are performed to validate the theoretical developments.
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