AN EXPERT SYSTEM FOR DIGITAL SIGNAL TYPE CLASSIFICATION

Ataollah Ebrahimzadeh - Maryam Ebrahimzadeh

   Because of rapid growing of radio communication technology of late years, importance of automatic classification of digital signal type is rising increasingly. Most of the proposed techniques can only classify low order of digital signals and/or a few kinds of digital signal. They usually require high levels of signal to noise ratio (SNR). This paper presents an expert technique that classifies a variety of digital signals. In this technique a multilayer perceptron neural network with self-adaptive step-size learning algorithm is proposed for determination the membership of the received signal. A combination of the higher order moments and the higher order cumulants up to eighth are proposed for extraction the prominent characteristics of the considered digital signals. In this technique, we have proposed a genetic algorithm for feature selection in order to reduce the number of features. Simulation results show that the proposed technique has high performance for identification the different digital signal types even at very low SNRs.

Keywords: statistical pattern recognition, signal type classification, SASS learning algorithm, a combination of the higher order moments and higher order cumulants, genetic algorithm

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