单通道通信信号的盲源分离算法研究

发布时间：2018-03-30 02:28

本文选题：通信信号　切入点：单通道盲源分离　出处：《兰州理工大学》2014年硕士论文

【摘要】：随着现代通信技术的发展和国防科技信息化进程的加快,全球通信业务需求迅速增长,无线通信基站数量急剧增多,使得电磁通信环境复杂化,频谱资源利用紧张化,干扰噪声种类多样化；进而导致单通道时频混叠信号在军用电子侦察、无线电频谱监测、紧急救援等通信应用环境中普遍存在。对于单通道盲源分离的方法,利用阵列信号处理的传统盲源分离算法以及时频域、空域和码域滤波的方法都不再适用。因此,研究如何在复杂无线通信环境中实现单通道盲源分离具有重要意义。本文在分析和归纳总结现有盲源分离基本原理和方法的基础上,探究了通信信号循环谱域的可分离机理及构建循环谱域滤波器实现单通道通信信号的盲源分离方法。研究发现循环平稳性可以很好的反应通信信号的本质特征,从通信信号的循环谱估计方法及循环谱性质出发,提出一种基于时变ARV模型的循环谱估计算法。算法将通信信号用时变ARV模型表示,通过基时间函数展开将线性非平稳问题转化为线性时不变问题,利用协方差矩阵以及谱相关理论估计出信号的循环谱；同时对一些常见调制信号循环谱进行验证,理论分析证明循环谱反应了调制信号载频和码元周期,通过循环谱估计可确定循环谱域滤波器的频移量。理论分析证明,通信信号在循环谱域具有独立性和稀疏性,可以通过滤波的方法实现单通道通信信号的盲源分离。为此,本文在维纳滤波器及盲自适应频移滤波器结构和原理的基础上,将已确定的频移量应用于线性共轭线性频移滤波器中得到一种能同时分离出两路源信号的单通道盲源分离方法。最后,在MATLAB环境下对混有白噪声的QPSK和BPSK两路时频重叠信号进行仿真验证,结果表明该方法可有效分离出两路源信号。
[Abstract]:With the development of modern communication technology and the acceleration of national defense science and technology informatization process, the demand for global communication services is growing rapidly, the number of wireless communication base stations is increasing rapidly, which makes the electromagnetic communication environment complicated and the utilization of spectrum resources tense. The variety of interference noise leads to the existence of single-channel time-frequency aliasing signals in military electronic reconnaissance, radio spectrum monitoring, emergency rescue and other communication applications. The traditional blind source separation algorithm based on array signal processing is no longer applicable in time-frequency domain, spatial domain and code domain filtering. Therefore, it is of great significance to study how to achieve single-channel blind source separation in complex wireless communication environments. On the basis of analyzing and summarizing the basic principles and methods of blind source separation, this paper probes into the detachable mechanism of cyclic spectrum domain of communication signal and the blind source separation method of single channel communication signal by constructing cyclic spectral domain filter. It is found that the cyclic stationarity can well reflect the essential characteristics of the communication signal. Based on the method of cyclic spectrum estimation and the properties of the cyclic spectrum of the communication signal, A cyclic spectrum estimation algorithm based on time-varying ARV model is proposed, in which the communication signal is represented by time-varying ARV model, and the linear non-stationary problem is transformed into a linear time-invariant problem by the basis time function expansion. The cyclic spectrum of signals is estimated by covariance matrix and spectral correlation theory, and the cyclic spectrum of some common modulated signals is verified. The theoretical analysis shows that the cyclic spectrum reflects the carrier frequency and symbol period of modulated signals. The frequency shift of cyclic spectral domain filter can be determined by cyclic spectrum estimation. The theoretical analysis shows that the communication signal is independent and sparse in the cyclic spectral domain, and can be separated from the blind source of the single channel communication signal by filtering. Based on the structure and principle of Wiener filter and blind adaptive frequency shift filter, Applying the determined frequency shift to linear conjugate linear frequency shift filter, a single channel blind source separation method can separate two source signals simultaneously. Finally, Two time-frequency overlapped signals of QPSK and BPSK mixed with white noise are simulated in MATLAB environment. The results show that the proposed method can effectively separate two source signals.
【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】：TN911.23

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