| 摘要: |
| 基于主成分分析（PCA）和独立成分分析（ICA），提出了一种新的调制分类算法。算
法采用PCA对样本数据降维、去除冗余成分，采用FastICA方法提取分类特征；采用支持
矢量机（SVM）作为分类器，以解决数据在低维空间中的不可分问题。该算法具有较低的复
杂度和较高的训练速度。仿真表明，与最大似然（ML）算法相比，算法仅具有1.8 dB的信噪
比损失，在Rayleigh慢衰落信道和中速运动的条件下，算法对5种QAM调制类型具有较好的
分类性能。 |
| 关键词: 通信信号 调制分类 主成分分析 独立成分分析 支持矢量机 |
| DOI：10.3969/j.issn.1001-893x.2013.07.008 |
|
| 基金项目: |
|
| Modulation classification algorithm based on PCA and ICA |
| FENG Xiang,CHEN Liang-bin |
| () |
| Abstract: |
| A principal component analysis(PCA) and independent component analysi
s(ICA) based modulation classification algorithm is presented. The samples are
first processed by PCA to reduce their dimension and eliminate their redundancie
s, and then the classification features are obtained by the FastICA algorithm. T
he Support Vector Machine(SVM) is applied to solve the non-separable problem
in low dimension space. The algorithm is less complex computationally and has fa
ster classifier training speed compared with other algorithms. The extensive sim
ulation results show that the proposed algorithm has only 1.8 dB SNR loss, a
nd exhibits better classification performance under Rayleigh channel and medium
movement condition. |
| Key words: communication signal modulation classification principal component analysis independent component analysis support vector machine |
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