针对突发自适应调制信号中的PSK和QAM调制方式识别问题,本文提出了一种能够识别BPSK、QPSK、8PSK以及16QAM、32QAM、64QAM、128QAM、256QAM八种信号类型的盲识别算法.该算法首先对信号的循环平稳性进行了分析和讨论,给出了利用循环高阶累积量的特征实现信号识别分类的理论依据.然后,提出了三种基于循环累积量的特征分别实现了QAM和PSK类间识别、MPSK类内识别以及方形QAM与十字形QAM的识别.最后通过对MQAM信号的瞬时幅度分布特性的深入研究和分析,提出了一种基于瞬时包络平方的方差的特征实现了QAM的类内识别.该算法选择了二叉树支持向量机作为识别分类器,并设计了一种新的识别流程完成了对上述信号调制方式的识别.该算法无需精确同步,对载波相位具有较好的鲁棒性,并能够对中频信号进行识别.仿真实验表明,该算法能够实现在较低信噪比条件下突发信号的识别.%For the purpose of the technology of modulation recognition of MPSK and MQAM modulation in burst adaptive modulation signals, a new blind identification algorithm is proposed. This algorithm can recognize eight kinds of modulation signals, including BPSK signals, QPSK signals,8PSK signals, 16QAM signals, 32QAM signals, 64QAM signals, 128QAM signals and 256QAM signals. Firstly the cyclostationarity of the signals is analyzed and discussed and the theoretical basis of the signal identification based on the features of cycle High-order Cumulation is given. Secondly three features are proposed based on cyclic cumulation of the signals to classify PSK signals and QAM signals, MPSK signals, square QAM signals and cross QAM signals respectively. Finally, through research and analysis of the instantaneous amplitude distribution of MQAM signals, a new feature based on the variance of instantaneous envelope square is proposed to classify the MQAM signals. The algorithm uses the binary tree support vector machine as classifier and designs a new identification process to classify the signals mentioned above. It does not require precise synchronization and is robust to the carrier phase. Moreover, it is suitable for the IF signals recognition. Simulation results show that the algorithm can achieve identification of burst signals under low SNR.
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