基于循环平稳理论的数字调制信号识别研究

发布时间：2018-06-06 09:52

本文选题：调制识别 + 循环平稳理论　；参考：《兰州理工大学》2014年硕士论文

【摘要】：复杂电磁环境下的数字调制信号自动识别是根据少有的先验知识将一个未知调制方式的通信信号归入相应的调制种类,为后续的进一步信号分析与处理提供可靠依据。在频谱监测、紧急救援与电子对抗等场合中,调制识别过程有着重要的作用。近几十年,学者们在此方向做了很多相关的研究工作,并且提出了一些可靠的通信信号调制识别算法。但是研究发现,很多方法在复杂通信电磁环境下工作的效率不是很好。因此,本文基于调制识别方面的国内外最新研究成果,在复杂电磁环境下,结合循环平稳理论、数字信号处理理论,研究如何利用能够有效表征信号差异的特征完成对数字调制信号的识别。在此研究背景下,本文主要工作包括以下几点： (1)对经典的基于统计特征的调制识别算法进行了研究与分析,根据特征所提取信息的变换特性,将其分为两大类：基于瞬变信息与缓变信息的统计特征,并对他们进行了综述。 (2)通过对调制信号经过非线性系统后功率谱的研究,发现不同的数字通信调制信号具有不同的离散谱线,本文利用AR模型提取信号的谱线特征,实现对常见数字调制信号的识别,并在MATLAB环境下仿真验证。 (3)根据通信信号循环平稳特性与LPTV模型的对应关系,将数字调制信号构建为相应的LPTV模型,估计模型的结构参数,在此基础上提取相应的循环谱特征,完成对数字调制信号的识别。
[Abstract]:Automatic recognition of digital modulation signals in complex electromagnetic environment is to classify a communication signal of unknown modulation mode into corresponding modulation types according to rare prior knowledge, which provides a reliable basis for further signal analysis and processing. Modulation recognition plays an important role in spectrum monitoring, emergency rescue and electronic countermeasures. In recent decades, scholars have done a lot of research in this direction, and proposed some reliable communication signal modulation recognition algorithm. However, it is found that many methods are not efficient in complex communication electromagnetic environment. Therefore, based on the latest research results of modulation recognition at home and abroad, in the complex electromagnetic environment, combined with the cyclic stationary theory, digital signal processing theory, This paper studies how to use the features which can effectively represent the difference of signal to realize the recognition of digital modulation signal. In this context, the main work of this paper includes the following: 1) the classical modulation recognition algorithms based on statistical features are studied and analyzed. According to the transformation characteristics of the information extracted from the features, they are divided into two categories: the statistical features based on transient information and slow change information, and they are summarized. 2) by studying the power spectrum of modulation signal after passing through nonlinear system, it is found that different digital communication modulation signal has different discrete spectral lines. In this paper, AR model is used to extract the spectral line characteristics of the signal. The recognition of common digital modulation signals is realized and simulated in MATLAB environment. 3) according to the corresponding relationship between the cyclic stationary characteristic of the communication signal and the LPTV model, the digital modulation signal is constructed into the corresponding LPTV model, the structural parameters of the model are estimated, and the corresponding cyclic spectrum features are extracted. The recognition of digital modulation signal is completed.
【学位授予单位】：兰州理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.3

【参考文献】