Signal Analysis and Classification of Digital Communication Signals using Adaptive Smooth-Windowed Wigner-Ville Distribution

Signals in a non cooperative communication environment such as in the high frequency (HF) spectrum are generally unknown in nature. There is a need for true spectrum monitoring where the information of the signal is made known as in spectrum management and intelligence gathering. In the spectrum surveillance system, the features that are estimated 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 spectrum monitoring system which is capable to analyze and classify the basic types of digital modulation signals such as ASK and FSK, and the more complex M-ary FSK. First, the signal is represented optimally using adaptive smooth-windowed Wigner-Ville (SWWVD) representation. The separable kernel of SWWVD is designed from the input signal to give the most optimal representation for each signal. Then various signal parameters is extracted from the representation for classification. A rules based approach is used as a classifier. The robustness of the system is tested in the presence of additive white Gaussian noise. On the average, the classification accuracy is 70 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.