Automatic analysis and classification of digital modulation signals using spectogram time frequency analysis

Abstract

Automatic analysis and classification of signals is important for spectrum monitoring. It enables the system to monitor the conformance of frequency planning from the users. Spectrogram time-frequency analysis can be used to extract useful information from time-varying signals. The information can be used for signal classification. This paper describes the design and implement an automatic system to analyze and classify the basic types of digital modulation signals such as amplitude shift-keying (ASK), frequency shift-keying (FSK) and phase shift-keying (PSK). Analysis method is based on the spectrogram time frequency analysis and a rules based approach is used as a classifier. From the time-frequency representation, the instantaneous frequency is estimated which is then used to estimate the modulation type and its parameters. This information is further used as input to the rules based classifier. The robustness of the system is tested in the presence of additive white Gaussian noise. On the average, the classification accuracy is 90 percent for signal-to-noise ratio (SNR) of 2 dB. Thus, the results show that the system gives reliable analysis and classification of signals in an uncooperative communication environment even if the received signal is weak.