通信信号调制样式识别技术

【作者】 赵星；

【导师】 李玉柏；

【作者基本信息】 电子科技大学 ， 信号与信息处理， 2006， 硕士

【摘要】 信号调制样式的自动识别是非协作通信中的主要问题,也是软件无线电接收机必备的功能之一。调制识别是近年来信号处理领域研究的热点问题,随着通信技术的飞速发展,通信信号的体制和调制样式变得更加复杂多样,信号环境日趋密集,使得常规识别方法和理论很难适应实际需要,无法有效地对通信信号进行识别,这也给通信信号的识别研究提出了更高的要求。近几十年来,人们在通信信号的识别方面作了大量有益的探索,提出了很多新思路和新方法。本文介绍了常用的通信信号(包括模拟调制样式和数字调制样式)的时域、频域特征,首先从时频域提取信号的特征参数,用于区分2ASK、4ASK、2FSK、4FSK、BPSK、QPSK、16QAM、64QAM这8种信号,用仿真信号和实际采样信号验证了该方法的可行性。接着,本文介绍了高阶累积量的定义和多种数字调制信号高阶累积量的值,通过计算基带信号的高阶累积量,可以对4ASK、2FSK、4FSK、BPSK、QPSK、16QAM六种信号进行分类,利用仿真信号和实际采样信号对该方法进行了论证,该方法可以在低信噪比条件下实施。此外,本文还讨论了小波变换的原理,并且采用离散小波变换提取特征参数,对DSB、2ASK、FM/PM、2FSK、2PSK信号进行分类,采用仿真信号对该方法进行了试验验证。在分类器设计方面,介绍了利用神经网络进行模式识别的原理,采用前述的三种特征提取方法,分别结合判决树分类器和神经网络分类器对信号进行分类,并且进行了试验论证。本文针对上述三种特征提取方法和两种分类器,在不同的信噪比下对仿真数据和实际采样数据进行了多次验证试验,取得了令人满意的试验结果。这些试验结果也反映了利用上述三种不同特征提取方法和两种不同分类器对信号进行识别的效果和性能,本文总结了这些不同方法的优缺点。同时,本文还研究了采用高阶累积量估计MPSK信号符号信噪比的方法和利用连续小波变换估计数字调制信号符号速率的方法,利用仿真信号证明了这两种方法的可行性。本文在通信信号调制样式的自动识别方法上做了一点有益的探索,在这方面还有大量的工作值得探索和实践。更多还原

【Abstract】 Automatic recognition of modulation signals is the key problem in non_cooperative communication systems. It is the required function of Software Defined Radio Receiver. With rapidly developing of communication technology, the system and modulation manner of communication signals have become more and more complicated and circumstance of signals has become increasing denseness. It results in that the routine methods and theory of recognition can hardly satisfy practical requirements and can’t effectively recognize communication signals. So the strict demand has been presented for studying on recognition of communication signals.For the last several decade years, the people have helpfully explored many methods of solving the question of recognition of communication signals. This thesis presents the time domain character and frequency domain character of common used signals, including digital modulation signals and analogue modulation signals. The first method is to extract feature parameters from time domain and frequency domain. It can distinguish 2ASK, 4ASK, 2FSK, 4FSK, BPSK, QPSK, 16QAM, 64QAM and it is demonstrated by simulation data and sampled data. Then, the definition of high order cumulants and the value of digital modulation signals’high order cumulants are presented. According to calculate the value of base band signals’high order cumulants, 4ASK, 2FSK, 4FSK, BPSK, QPSK, 16QAM can be distinguished. This method is demonstrated by simulation data and sampled data. It can be implemented in lower SNR condition. In addition, the thesis presents the theory of wavelet transform and method to recognize DSB, 2ASK, FM/PM, 2FSK, 2PSK by discrete wavelet transform. It is demonstrated by simulation data. As for classifier, it presents the artificial neural network. Based on three methods of modulation recognition and decision tree classifier and neural network classifier, experimentations have been carried through.The experimental results are satisfying, which are gained in different SNR condition. Those results reflect the capability and performance of different methods. The thesis summarizes their strongpoint and weakness.更多还原