Modulation Recognition Of Communication Signals Based On Multi-characteristics Parameter Study

Modulation identification for communication signals is a still important problem in the intercepted signal processing, it is required to identify the modulation format and modulation parameters in the complicated signal environment with noise, and to provide reference for farther analysis and processing. Modulation format is one of the most important characteristics used to distinguish communication signals, After analyzing the received signal, the objective of modulation identification is to decide the modulation format and estimate the modulation parameters of the communication signal without any priori knowledge about the signal information content. With the development of communication technology, the spatial signals are more and more complicated and dense. It results in that output of the receiver doesn't contains only one signal, which makes the modulation identification more difficult, And as results, there comes more demands for the research of modulation identification of communication signals.For the last several decades, researchers explored many methods of to solve the problem of the modulation identification for different modulation signals. Based on the research before, this dissertation has a farther research on the modulation identification of communication signals. The main contributions are list as follows:1. We analyze the blind parameter estimation of intercepted signal. Without prior information of signal, we can estimate some parameters of signal, such as signal to noise ratio (SNR), carrier frequency, and symbol rate of digital modulation signals. These parameters are advantaged to the modulation identification of communication signals.2. For processing the received communication signal, firstly we propose a method, which uses the symbol rate extracted by wavelet transform to classify the analog modulation signals and the digital modulation ones; for analog modulation signals, we use the envelop feature, the power spectrum of normalized-centered instantaneous amplitude and the power spectrum symmetry to classify AM, FM, DSB, USB, LSB; while for digital modulation signals, we propose a new feature after farther researching the high order cumulants, and use the envelop feature and high order cumulants of the received signal to classify MASK, MPSK, MFSK. Simulations indicate that this method can automatically classify communication signals when .3. For co-channel multiple digital modulation signals, we proposed two methods: the histogram of the power spectrum of the segmented signals and the symbolic rate extracted by wavelet transform, and simulations are given to compare with each other.