Spectogram time-frequency analysis and signal classification of digital modulation signals

Abstract

A non cooperative communication environment such as in the HF (high frequency) spectrum is when the signals present are unknown in nature. This is essentially true spectrum monitoring that is an activity in spectrum management and intelligence gathering. An instrument that is used for this purpose is a spectrum surveillance system whose features are: the measurement of signal strength and carrier frequency, the location of transmitters, estimation of modulation parameters and the classifications of signals. This paper describes the design and implement a 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.