Communication Signal Modulation Identification And Parameter Estimation

This paper studies the methods of modulation type identification and parameters estimation of communication signal.Modulation recognition of communication signals has been widely used in signal confirmation, interference identification, radio intercept, electronic warfare and signal monitoring. modulation type identification and characteristic parameters extraction of communication signal are important issues in modern electronic warfare under the circumstance of complex electromagnetic environment.First, this paper discusses how to produce a variety of communication signals and analyzes the basic characteristics based on their time domain expression. And then based on these characteristics using the decision theory algorithm to identify the modulation type. This algorithm defines four characteristic parameters to recognize analog modulation type and five characteristic parameters to identify digital modulation type. The simulation results show that with the improvement of signal to noise ratio, the corresponding characteristic parameters of different modulation signal has obvious differences. So it has good performance in distinguishing different modulation types. And then by defining nine characteristic parameters, this paper introduces the analog and digital modulation recognition algorithm under the premise of no priori type information about the intercepted signal. The simulation results show that the success rate is about 90% when SNR is greater than 15 d B. So this algorithm is effective. This paper also applies higher order cumulants to recognize digital signal modulation type. The results show that this method has better performance in recognizing digital modulation type with regard to the decision theory algorithm.This paper has also estimated other important signal parameters, such as carrier frequency, symbol rate, signal to noise ratio, nonlinear phase, these parameters are essential when recognize and demodulate the intercepted signals. This paper has a detailed analysis of each parameter estimation method. Combined with the results of the simulation results, advantages and disadvantages of each method are also analyzed.Computer simulation results show the effectiveness of the various algorithms studied in this paper.