摘要
为了提高数字信号调制模式识别在低信噪比下的正确率,在对复杂度理论加以分析的基础上,提出了一种新的特征提取方法。该方法首先引入希尔伯特-黄变换求得样本的边际谱,然后利用分形和Lempel-Ziv复杂度的方法提取用于调制识别的特征参数,最后利用RBF神经网络分类器进行数字信号调制模式的分类识别。仿真结果表明该算法具有较好性能。
On the basis of the marginal spectrum and complexity theory, a new feature extraction method is proposed to improve the accuracy of the digital modulation recognition under the low signal-to-noise ratio. Firstly, the Hilbert-Huang Transform is put forward to obtain the marginal spectrum of the samples. Secondly, the fractal dimensions and the Lempel-Ziv complexity of the samples after Hilbert-Huang Transform are calculated to extract the feature parameters. Finally, the identification problem is solved by using artificial neural network. The simulation results verify the performance of the proposed algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第4期226-231,共6页
Computer Engineering and Applications
基金
国家自然科学基金项目(No.61040007)
安徽省电工电子与自动化省级示范实验实训中心项目(No.20101687)
关键词
调制识别
边际谱
复杂度
RBF神经网络
分形原理
modulation recognition
marginal spectrum
complexity measure
RBF neural network
fractals theory
