摘要
针对传统数字调制识别方法在非高斯Alpha稳定分布噪声下识别性能差的问题,该文提出一种基于广义分数阶傅里叶变换和分数低阶Wigner-Ville分布的数字调制识别新方法。该方法提取广义分数阶傅里叶变换的零中心归一化瞬时幅度谱密度的最大值和分数低阶Wigner-Ville分布幅度的最大值作为识别特征参数,并采用判决树分类器,实现了非高斯噪声下数字调制信号识别。仿真结果表明,在非高斯Alpha稳定分布噪声下,该识别方法不仅性能明显优于传统方法并且具有较高的识别率和良好的稳健性。
In an Alpha stable distribution noise environment, the traditional methods of digital modulation signals recognition have the problems of poor performance. A novel recognition method based on generalized fractional Fourier transform and fractional lower Wigner-Ville distribution is proposed to solve this problem. This method extracts the recognition characteristic parameters which are maximum of normalize and center instantaneous amplitude spectral density based on generalized fractional Fourier transform and maximum of fractional lower Wigner-Ville distribution amplitude. And then the method uses decision tree as a classifier to achieve digital modulation signals recognition. Simulation results show that the proposed method not only has better performance than the traditional recognition methods but also has higher recognition rate and good robustness in an Alpha stable distribution noise environment.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第1期85-91,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61271299)
国家863计划项目(2007AA01Z288)
高等学校学科创新引智计划项目(B08038)资助课题
