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中文摘要：
在高信噪比处理域构造新的用于调制识别的高阶统计量幅值特征，与传统特征相比保留了更多的分类信息，适合干扰较大多种调制模式并存的环境。基于联合特征向量有效提高了识别性能，用窗口平滑抑制截获信号中的噪声，对识别器输入特征向量样本规范化以提高处理速度。分别基于欧氏距离分类方法和改进算法的神经网络识别器进行仿真实验，证明了采用联合特征向量和优化方法在低信噪比干扰更大的信道条件下能区分更多的调制类型（MASK、MPSK、MFSK、MQAM），且平均调制识别率提高20％～30％，算法效率也得到明显提高。
英文摘要：
In the high SNR processing domain, proposed novel high order statistic amplitude features and optimization method to preserve more classification information for various modulation types. The method based on the combined feature vector improved the algorithm performance compared to conventional features. In addition, adopted linear smoothing of the intercepted signal and normalization of input feature vector to restrain the noise and reduce the training time. Based on the Euclidean distance classification method and modified neural network recognizer, the simulation results verify the novel feature vector and optimization improve the average probability of correct classification by about 30% for more modulation types （ MASK, MPSK, MFSK, MQAM） at low SNR with greater interference. The algorithm efficiency is also improved markedly.
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