联合时域和时频域特征的数字调制信号自动分类

Digital Modulations Automatic Classification Using the Combination of Several Features Extracted from Time and Time-Frequence Domain

针对ASK、PSK、QAM、FSK、MSK、LFM、OFDM信号的调制分类问题,在无任何先验知识的条件下,本文提出了一种基于模糊函数域、Choi-William分布时频域和时域特征的自动分类算法。该算法首先提取信号在不同域上的特征,然后通过主成份分析法降维去除特征之间的冗余信息,再利用支持向量机分类器实现信号分类。为评估算法性能及优点,本文在不同信噪比和调制信号参数条件下,做了一系列仿真实验,并将本文算法与其他分类识别算法进行了对比。最后结果验证了本文算法的可行性和有效性,以及在低信噪比条件下较好的鲁棒性。

This paper presents a method for the automatic classification of digital modulations including ASK, PSK, QAM, FSK, MSK, LFM, OFDM, without a priori knowledge of the signal parameters by a large set of features which are extracted from ambiguity function （ AF）, Choi-William distribution （CWD） and time domain. In this method the whole features are extracted and pruned by discarding redundant features using principal component analysis （PCA） technique, then inputted to the SVM-based classifier for decision-making. To evaluate the method performance, some simulations have been carried out in different conditions of signal noise ratio and the main parameter. To assess the advantages, comparison with other classification methods has been given. The final results verified the feasibility and effectiveness of the algorithm in this paper, which also proved the good robustness of the method in condition of low signal noise ratio.