Detection And Modulation Identification Of Communication Signals

With the rapid development of modern science and technology, the electromagnetic interference and the noise becomes more and more. And the electromagnetic environment becomes increasingly sophisticated, How to find a simple and effective method to detect the target signals and identify its modulation, is an important research topic on military and civilian communications, especially in the non-cooperative communication field.However,it’s still a huge challenge in the modulation indentification field because of the existence of numerous different modulation types, the effection of channel to signals and the interference of noise, It has become a hot research in the field of signal detection and modulation identification at present.This paper introduces the concept of signal detection, the classification of signal modulation recognition, investigates and analyses the research status at home and abroad, and provides comprehensive analysis of various signal detection technology,include the classical algorithm such as energy detection,matching filter detection and cycle property detection method.And the performance of the algorithms is analyzed through simulation in this paper. Also,this paper provide a brief explanation of the definition and the theoretical basis of wavelet transforms,and it describes the concept of the triangle quasi-Haar wavelets and its permit conditions. The triangle quasi-Haar wavelet is applied to the signal modulation recognition for the first time. Morever, a method is proposed to realize the inter-class and intra-class modulation identification between PSK,FSK and QAM and by conjunctive use of the triangle quasi-Haar wavelets, the ratio of the variance of the envelope to the square of the mean of the envelope, amplitude statistical. Finally, the whole system is simulated by MATLAB.The result shows that while SNR is more than 11 d B, PSK, FSK and QAM are identified correctly more than 90%.The method can complete the signal modulation identification well in low SNR. And the results of the simulation prove an excellent anti-noise performance and practicability of the method.