基于循环谱的信号参数估计与调制方式识别
发布时间：2018-11-03 15:01
【摘要】：通信信号的调制方式识别和参数估计对于军用电子对抗以及民用频谱监管都具有极其重要的意义。由于无线通信环境的复杂性和不可预知性,在低信噪比条件下,传统的参数估计和调制方式识别方法难以达到预期的效果。基于循环谱分析的信号处理具有较强的抑制噪声能力,已被广泛应用到参数估计和特征参数提取等方面,但由于循环谱理论分析方法还存在很多问题,本文在阅读国内外最新的思想和研究后,研究用新的一些方法对信号进行参数估计和调制方式识别。本文首先分析了几种常用数字和模拟调制信号的循环谱周期图,并通过频域平滑算法分析了信号载频和码元速率与频率分辨率的奇偶性对循环谱估计的影响,推导出基于循环谱的联合截面搜索算法。其次为解决高阶MPSK信号因为倍频及平方变换引起的随机噪声问题,提出通过对信号进行希尔伯特变换的方法达到降低噪声的目的。另外,通过数学推导和实验仿真验证分析了调制信号在谱特征截面的分类特征参数以及基于循环谱分析的调制信号识别流程,并且分析了矩阵抽取算法,引入了相像系数并给出适用于低信噪比情况下的特征参数,通过仿真验证了该算法的引入在低信噪比条件下可得更好的识别性能。最后分析了影响矩阵抽取算法的两个主要因素抽取系数积与离散傅里叶变换点数对算法性能的影响,通过仿真推出抽取系数积不宜过大且离散傅里叶变换点数不宜太小的情况下才能得到较好的识别效果。
[Abstract]:Modulation mode identification and parameter estimation of communication signals are of great significance for military electronic countermeasures and civil spectrum regulation. Due to the complexity and unpredictability of wireless communication environment, the traditional methods of parameter estimation and modulation recognition are difficult to achieve the desired results under the condition of low signal-to-noise ratio (SNR). The signal processing based on cyclic spectrum analysis has strong ability to suppress noise, which has been widely used in parameter estimation and feature parameter extraction, but there are still many problems in the theory of cyclic spectrum analysis. After reading the latest ideas and researches at home and abroad, this paper studies some new methods for parameter estimation and modulation recognition. In this paper, the cyclic spectral periodic diagrams of several commonly used digital and analog modulated signals are first analyzed, and the influence of signal carrier frequency, symbol rate and frequency resolution on cyclic spectrum estimation is analyzed by frequency domain smoothing algorithm. A joint cross section search algorithm based on cyclic spectrum is derived. Secondly, in order to solve the problem of random noise caused by frequency doubling and square transformation in high-order MPSK signals, the method of Hilbert transform is proposed to reduce the noise. In addition, the classification characteristic parameters of modulation signal in spectral feature section and the recognition flow of modulation signal based on cyclic spectrum analysis are analyzed by mathematical derivation and experimental simulation, and the matrix extraction algorithm is analyzed. The image coefficient is introduced and the characteristic parameters suitable for low signal-to-noise ratio (SNR) are given. The simulation results show that the proposed algorithm can achieve better recognition performance under the condition of low signal-to-noise ratio (SNR). Finally, the effects of two main factors, the extraction coefficient product and the number of discrete Fourier transform (DFT), on the performance of the algorithm are analyzed. It is concluded by simulation that the extraction coefficient product should not be too large and the number of discrete Fourier transform points should not be too small in order to obtain a better recognition effect.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.3
本文编号：2308133
[Abstract]:Modulation mode identification and parameter estimation of communication signals are of great significance for military electronic countermeasures and civil spectrum regulation. Due to the complexity and unpredictability of wireless communication environment, the traditional methods of parameter estimation and modulation recognition are difficult to achieve the desired results under the condition of low signal-to-noise ratio (SNR). The signal processing based on cyclic spectrum analysis has strong ability to suppress noise, which has been widely used in parameter estimation and feature parameter extraction, but there are still many problems in the theory of cyclic spectrum analysis. After reading the latest ideas and researches at home and abroad, this paper studies some new methods for parameter estimation and modulation recognition. In this paper, the cyclic spectral periodic diagrams of several commonly used digital and analog modulated signals are first analyzed, and the influence of signal carrier frequency, symbol rate and frequency resolution on cyclic spectrum estimation is analyzed by frequency domain smoothing algorithm. A joint cross section search algorithm based on cyclic spectrum is derived. Secondly, in order to solve the problem of random noise caused by frequency doubling and square transformation in high-order MPSK signals, the method of Hilbert transform is proposed to reduce the noise. In addition, the classification characteristic parameters of modulation signal in spectral feature section and the recognition flow of modulation signal based on cyclic spectrum analysis are analyzed by mathematical derivation and experimental simulation, and the matrix extraction algorithm is analyzed. The image coefficient is introduced and the characteristic parameters suitable for low signal-to-noise ratio (SNR) are given. The simulation results show that the proposed algorithm can achieve better recognition performance under the condition of low signal-to-noise ratio (SNR). Finally, the effects of two main factors, the extraction coefficient product and the number of discrete Fourier transform (DFT), on the performance of the algorithm are analyzed. It is concluded by simulation that the extraction coefficient product should not be too large and the number of discrete Fourier transform points should not be too small in order to obtain a better recognition effect.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN911.3
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