摘要
针对经验模态分解(empirical mode decomposition,EMD)过程中本征模态函数(intrinsic mode function,IMF)上存在脉冲星信号与噪声混叠的问题,提出了一种基于局部峰度检验加窗的EMD消噪方法。首先,利用自相关和互相关来判断IMF的重构起点;其次,通过局部峰度检验方法来获取重构起点前两层IMF中信号脉冲部分的左、右端点,选用Turkey-Hanning窗滤掉脉冲外噪声;最后,利用自适应阈值方法进一步除噪,改善信号质量。实验结果表明,与其他5种方法相比,所提消噪方法可以有效抑制噪声,保留脉冲星信号细节信息,具有更高的消噪性能。
For the problem that pulsar signal and noise are aliasing on the intrinsic mode functions （IMF） during the empirical mode decomposition （EMD）, a denoising method based on EMD with kurtosis test window is proposed. Firstly, the starting point for IMF refactoring is calculated by auto-correlation and cross-correlation. On that basis, the left and the right endpoints of the pulse part of signal from the former two IMFs of the starting point for refactoring are acquired by the local kurtosis test. Finally, the adaptive threshold method is used to remove noise and improve signal quality. The experimental results show that, compared with the other five methods, the proposed method has higher denoising performance, which can effectively eliminate the noises and retain the details in pulsar signal.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2017年第6期1208-1214,共7页
Systems Engineering and Electronics
基金
国家自然科学基金(61004122)
陕西省自然科学基金(2015JM6270)
校科技创新计划项目(2015CX027
2016CX043)资助课题
关键词
脉冲星
经验模态分解
峰度检测
时域加窗
自适应阈值滤波
pulsar
empirical mode decomposition （EMD）
kurtosis detection
time domain windowing
adaptive threshold filter
