【作者】 位小记；

【导师】 谢红；

【作者基本信息】 哈尔滨工程大学 ， 通信与信息系统， 2011， 硕士

【摘要】 通信信号的调制方式识别是截获信号处理领域的重要组成部分,它需要在复杂环境和有噪声干扰的条件下,不依赖于其它的先验知识,确定接收信号的调制方式,并提取相应的调制参数,为信号的进一步分析和处理提供依据。随着通信体制和信号调制方式的复杂化和多样化,通信环境日益复杂,使通信信号的调制方式识别成为军、民领域内十分重要的研究课题。近几十年来,人们在通信信号调制方式识别方面进行了大量的研究,并提出了许多新的方法和思路。本文在已有方法的基础上,重点对常用数字通信信号调制方式的自动识别进行研究。本文的主要工作和创新之处在于：首先,本文分析了常用数字调制信号的调制原理,并对基于希尔伯特变换的瞬时信息提取、常用的载波频率估计和码元速率估计方法进行了研究。其次,针对数字调制方式识别在低信噪比下的应用,本文对基于瞬时特征参数的数字调制方式识别算法进行了两点改进：首先设计一种小波滤波器,在信号的预处理阶段对瞬时信息进行小波消噪处理,减弱噪声的影响；另外从中频信号中提取五个相对简单的瞬时特征参数,改善算法的复杂度,简化识别过程。从而提高了算法在低信噪比下的识别性能。再次,本文在理论分析的基础上,提出了一种基于高阶累积量和星座图的联合数字调制方式识别算法。该算法针对较难识别的数字调制信号2FSK、4FSK和8PSK,将傅里叶变换与高阶累积量相结合,提取高性能的特征参数进行识别,同时采用改进的星座图聚类分析法提高MQAM信号的识别率。文中从理论上分析了该算法的有效性,并用计算机仿真验证了有效性。最后,为了改善数字调制方式识别在小样本下的识别效果,给出了一种基于高阶累积量和支持向量机的数字调制方式识别算法。该算法采用一对一的多类构造法,使用交叉验证网格搜索的方法进行参数选取,构建了稳健的多类支持向量机分类器,并利用高阶累积量域内构造的特征向量,进行数字调制信号的识别。仿真结果表明,与BP神经网络法相比,该算法具有更好的小样本识别性能和推广泛化能力。更多还原

【Abstract】 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.更多还原