Automatic classification of analog and digital modulation signals plays an important role in
communication application such as an intelligent demodulator, interference identification and
monitoring. The automatic recognition of the modulation format of a detected signal, the intermediate
step between signal detection and demodulation, is a major task of an intelligent receiver, with various
civilian and military applications. This paper presents a new approach for automatic modulation
classification for digitally modulated signals. This method utilizes a signal representation known as the
modulation model. The modulation model provides a signal representation that is convenient for
subsequent analysis, such as estimating modulation parameters. The modulation parameters to be
estimated are the carrier frequency, modulation type, and bit rate. The modulation model is formed via
autoregressive spectrum modeling. The modulation model uses the instantaneous frequency and
bandwidth parameters as obtained from the roots of the autoregressive polynomial. This method is also
classifies accurately under low carrier to noise ratio (CNR). This paper is also presents an improved
version of S-Transform for time frequency analysis of different digitally modulated signals to observe
variations of amplitude, frequency and phase.
(EN)
