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As a consequence of recent developments in communica-tions, the parameters of communication signals, such as the modulation pa-rameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target sig-nals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recogni-tion method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classifi-cation processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suit-able for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which can-not be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy char-acteristics in time-varying SNR situations,offers accurate clustering perfor-mance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6dB. © 2013 The Institute of Electronics, Information and Communication Engineers.

A Robust Signal Recognition Method for Communication System under Time-Varying SNR Environment