Research On Modulation Types Recognition Of Digital Communication Signals

Modulation type recognition of communication signals is an important part in the field of intercepted signal processing, which needs to identify the modulation type and extract the modulation parameters of the received signals without any priori knowledge in a complex environment and noise interference conditions, and provide the basis for further analysis and signal processing. With the complication and the diversification of communication system and signal modulation type, the communication environment is more and more complicated, it makes that the research of the communication signals modulation type recognition becomes a very important topic in civil and military field.In recent decades the people have done much reseach on modulation type recognition of communication signals, at the same time many new methods and ideas are also proposed in this area as well. Based on the existing method, modulation type recognition of common digital communication signals is mainly studied in the paper. Main work and innovation of this paper include:Firstly, this paper analyses the modulation principle of common digital modulation signals, and studies instantaneous information extraction based on Hilbert transform, the common method of carrier frequency estimation and symbol rate estimation.Secondly, for the application under the low signal-to-noise(SNR), the algorithm based on instantaneous characteristic parameters is improved in two aspects:on the one hand, a wavelet filter is designed to de-noise the instantaneous information in pre-processing of received signals; on the other hand, five relatively simple instantaneous characteristic parameters extracted from IF signal are adopted to improve algorithm complexity and simplify recognition procedure. As a result, recognition performance of algorithm is improved under low SNR.Thirdly, given theoretical analysis, a recognition algorithm of digital modulation type based on high order cumulants(HOC) and constellation is presented in the paper. The algorithm combines Fourier transform with high order cumulants and extracts feature parameter of excellent performance to classify 2FSK,4FSK and 8PSK which are difficult to classify, and adopts improved constellation clustering analysis to improve the correct rate of MQAM modulation recognition. The paper analyses the effectiveness of algorithm in theory, and also verifies it by simulation experiment.Finally, the paper gives a digital modulation type recognition algorithm based on high order cumulants and support vector machines to improve recognition effect under small sample. In order to build robust SVM classifier, one against one of multi-classifer is designed, and the method of parameter selection using cross-validating grid is adopted. Then, characteristic vector extracted in high order cumulants domain is adopted to identify digital modulation types.Simulation results show that the algorithm possesses better small sample recognition performance and generalization ability comparing with BP neural network.