Modulation Recognition Of Communication Signals Using Spectral Correlation Approach

Modulation Recognition of Communication Signals is an important part in electronic counter measures; it also is a rapidly evolving area of signal analysis. The complexity of radio environment, especially the uncertainty of radio communication in the battlefield, traditional methods to parameter estimation and modulation recognition, based on stationary model, at low SNR fading situations is not a suited method. Different types of modulated signals that have identical power spectral density functions can have highly distinct spectral correlation functions. The different spectral correlation function characteristic can be used to classify modulated signals, even when the signals are buried in noise. Computer simulations for different types of digital communication signal corrupted by Gaussian noise have been carried out.Basic concepts of cyclostationarity are introduced systematically and the expressions of cyclic autocorrelation and cyclic spectrum of common modulations are deduced using linear periodically time-variant filtering model. Using the theory of Spectral correlation, this paper models the modulated signals as a cyclostationary random process, its properties can be characterized by the spectral correlation function. The paper used hardware data. Computer simulations for different types of communication signal corrupted by different Gaussian noise have been carried out. Theoretical analysis, simulation results and field tests show that algorithms presented in the thesis are not only creative in theory but also of great applied value.