Classifier Design On Automatic Recognition Of Modulation Of Communication Signals

The modulation recognition of communication signals is one of the key techniques of communication reconnaissance, and it is also an important task in the field of Electronic warfare. This dissertation has a study on the modulation identification of communication signals. It introduces how to extract characteristics of signals and how to design classifiers. Especially, it gives a feature set consisting of fractal dimension, L-Z complexity degree, entropy and resemblance coefficient and designs a classifier based on an algorithm called least squares support vector machine.Firstly, the dissertation introduces mathematical models of common communication signals and simulates to get basic signal figures. It lays the groundwork for future feature extraction.It researches three ways of characteristics extraction of signals including zero-center instantaneous feature, wavelet analysis feature and a feature set consisting of fractal dimension, L-Z complexity degree, entropy and resemblance coefficient. The experiment shows that features are effective for classification. It discusses congregation degree among same kinds and separation degree among different kinds of signals through drawing feature distribution figures. It shows that the feature set consisting of fractal dimension, L-Z complexity degree, entropy and resemblance coefficient congregates among same kinds and separates among different kinds highly. It can gain a high recognition rate of signals.To design the classifier, firstly, it researches how to design neural network. Then it analyzes support vector machines classifiers in theory at length and gives some algorithms for classification question beyond two kinds. On the one hand, based on structure risk minimization least squares support vector machine algorithm can gain best results with existing information. The condition of infinite stylebooks is unnecessary. So the outcome is good based on this algorithm when the stylebooks are few. On the other hand, it maps key features into the high dimension space. The...