| The automatic identification of the modulation types has been applied to many fields such as signal identification, interference identification, radio interception, monitoring, etc. The objective of automatic modulation identification is to decide the modulation type and estimate the modulation parameters without any priori knowledge about the signal information content. The paper studied the extraction of the instantaneous parameters and estimation of the symbol rate of communication signals. On the basis of previous research, the paper adopted a statistical pattern recognition framework and designed a combined classifier of fractal dimension and hierarchical architecture based on artificial neural networks (ANN). The performance of the algorithm is verified through plenty of computer simulations and the influence of data length for the correct identification probability is investigated. The paper also includes fuzzy decision algorithm. |
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