数字通信信号调制制式的识别研究

发布时间：2018-01-19 12:34

  本文关键词： 调制识别 特征提取 小波变换 高阶累积量　出处：《长春理工大学》2016年硕士论文　论文类型：学位论文

【摘要】：在现代通信技术中,通信信号的制式识别在电子对抗、频谱管理、自适应接收、认知无线电等多个领域有广泛的应用。在现有的文献中,通信信号的调制制式识别方法主要分为两种:基于决策理论的最大似然假设检验方法和基于特征提取的模式识别方法。第一种方法能够达到很好的识别效果,但是计算复杂,而且对频偏、相偏、定时误差等非常敏感。相比较而言,第二种方法计算复杂度低,效率高,在特征选取合理的时候能够达到很好的识别效果,且具有较高的稳定性。因此本文重点研究基于特征提取的模式识别方法。本文应用小波变换方法,对PSK、ASK、FSK信号提取了特征参量并对信号进行了有效的分类。在假定噪声为加性高斯白噪声的条件下建立了信号模型,说明了尺度因子的选取问题,通过计算小波变换系数幅值提取了信号的特征参数。通过MATLAB对通信信号进行了仿真,说明了算法的有效性。本文应用高阶累积量,针对多径信道条件,对BPSK信号和QPSK信号的制式识别进行了较深入研究。在无线通信中,实际信道通常有多径衰落现象。在这种情况下,建立在高斯白噪声信道模型上的调制识别算法的性能通常会下降甚至失效。针对此问题,提出了基于四阶累积量和六阶累积量相结合的调制识别算法。多径数目为2时,从理论上证明了该算法和基于四阶累积量的算法相比,能够更好的抗多径干扰。仿真结果表明,在多径衰落条件下,该算法对BPSK信号和QPSK信号的识别率高于基于四阶累积量的算法。在信噪比为2dB的多径衰落信道情况下,分类BPSK和QPSK信号的识别率几乎达到100%;在识别BPSK信号时,此算法性能明显优于基于四阶累积量的算法。
[Abstract]:In modern communication technology, communication signal recognition has been widely used in many fields, such as electronic countermeasure, spectrum management, adaptive reception, cognitive radio and so on. The modulation recognition method of communication signal is divided into two kinds: the maximum likelihood hypothesis test method based on decision theory and the pattern recognition method based on feature extraction. The first method can achieve good recognition effect. But the calculation is complex and sensitive to frequency offset, phase offset and timing error. Compared with other methods, the second method has low computational complexity and high efficiency, and can achieve a good recognition effect when the feature selection is reasonable. Therefore, this paper focuses on the research of pattern recognition based on feature extraction. In this paper, we apply wavelet transform method to PSK ask. The characteristic parameters of FSK signal are extracted and the signal is classified effectively. The signal model is established under the assumption that the noise is additive Gao Si white noise, which explains the selection of scale factor. The characteristic parameters of the signal are extracted by calculating the amplitude of the wavelet transform coefficient. The simulation of the communication signal by MATLAB shows the validity of the algorithm. In this paper, the high-order cumulant is applied. In view of the multipath channel condition, the standard recognition of BPSK signal and QPSK signal is deeply studied. In wireless communication, the actual channel usually has multipath fading phenomenon. In this case. The performance of modulation recognition algorithm based on Gao Si white noise channel model usually decreases or even fails. A modulation recognition algorithm based on the combination of fourth-order cumulant and sixth-order cumulant is proposed. The number of multipath is 2:00. It is proved theoretically that this algorithm is compared with the algorithm based on fourth-order cumulant. The simulation results show that under the condition of multipath fading, it can resist multipath interference better. The recognition rate of BPSK signal and QPSK signal is higher than that based on fourth-order cumulant. In the case of a 2dB signal-to-noise ratio (SNR) multipath fading channel. The recognition rate of classified BPSK and QPSK signals is almost 100. The performance of this algorithm is better than that based on fourth order cumulant in the recognition of BPSK signals.
【学位授予单位】：长春理工大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN911.3
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本文编号：1444208