Study On Features Of FSK Signal And PSK Signal

Identification of communication signals is a hot topic today.As one of the hot sopts in the field of information, identification of communication signals is widely used in the field of military and civil. Both identification of signals and researches on features of signals are bases of decoding, pattern recognition, object tracking and navigation. Comparing to identification of radar signals started earlier, identification of communication signals was started later, and there are not so rich, deep, diverse researches in identification of communication signals. For the phenomenon, this paper identity the signals of communication transmitters, including FSK signals and PSK signals which are widely used.In this paper, features are extracted from the specific signals respectively by the method using Hilbert transform and the method using Hilbert transform with wavelet transform, to compare the two methods. The simulation results indicate, according to the specific signals, the method using Hilbert transform only is able to finish the feature extraction of some signals, but the method using Hilbert transform with wavelet transform is able to finish the feature extraction of rest of the signals.In researches on characteristics of stable signals, most work is done on carrier frequency offset and modulation parameters. For carrier frequency offset, a method improved phase-based on carrier frequency offset is used to estimate the carrier frequency offsets of signals. Simulation results indicate the carrier frequency offsets of signals, comparing to the actual carrier frequency. For modulation parameters, most work is done on researches on symbol rate of FSK signals and PSK signals. For FSK signals, a method based on STFT rate of time-frequency energy and wavelet transform is used to estimate the symbol rate of FSK signals. Under certain conditions, the data of FSK transmitters is used to get feature classification map by symbol rate, clustering obtained bit rate characteristics of a good conclusion. For PSK signals, a method based on autocorrelation of wavelet transform is used to estimate the symbol rate of PSK signals. Under certain conditions, the data of FSK transmitters is used to get feature classification map by symbol rate, clustering obtained bit rate characteristics of a good conclusion.The subtle feature set is defined with subtle features. For the subtle feature set, a method based on neighborhood rough set is used to define the feature set. And a method based on "feature selection of variable precision neighborhood rough set" is used to assess the importance of subtle feature.At last, a classification reference is defined. In SVM, this algorithm can not only reduce the dimension of the feature set, but also evaluate the significance of every attribute. And it enhances the identification rate taking the significance of features comparing to single classification.