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基于整体经验模态分解的信噪比估计方法
引用本文:邢 辉,李国汉.基于整体经验模态分解的信噪比估计方法[J].太赫兹科学与电子信息学报,2014,12(3):445-448.
作者姓名:邢 辉  李国汉
作者单位:Postdoctoral Science Research Workstation of 69079 Troop,Urumqi Xijiang 830013,China;Postdoctoral Science Research Workstation of 69079 Troop,Urumqi Xijiang 830013,China
摘    要:为了提高未知样式信号的信噪比估计性能,提出一种基于噪声辅助的信噪比估计新算法,通过固有模态函数(IMF)分量平均周期的变化判断信号与噪声界限,给出了基于噪声辅助估计法的工作原理和流程图,分析了基于噪声辅助估计法的性能。仿真结果表明,基于噪声辅助估计法能够实现盲信号信噪比估计,在0 dB信噪比下均方误差不超过0.2 dB。

关 键 词:信噪比估计  经验模态分解  整体经验模态分解
收稿时间:2013/7/17 0:00:00
修稿时间:2013/8/30 0:00:00

A novel Ensemble Empirical Mode Decomposition-based algorithm of Signal to Noise Ratio estimation
XING Hui and LI Guo-han.A novel Ensemble Empirical Mode Decomposition-based algorithm of Signal to Noise Ratio estimation[J].Journal of Terahertz Science and Electronic Information Technology,2014,12(3):445-448.
Authors:XING Hui and LI Guo-han
Institution:(Postdoctoral Science Research Workstation of 69079 Troop, Urumqi Xijiang 830013, China)
Abstract:To enhance the Signal to Noise Ratio(SNR) estimation performance of unknown type signals, a novel algorithm based on noise-assisted is proposed, in which the boundary of the signal and noise is determined according to the average period curve of Intrinsic Mode Functions(IMF). The algorithm principle and its flow chart are presented, and the performance of noise-assisted method is also analyzed. Simulation results show that, noise-assisted method is adapted to unknown signals SNR estimation, and the mean square error is below 0.2 dB under SNR of 0 dB.
Keywords:Signal to Noise Ratio estimation  Empirical Mode Decomposition(EMD)  EnsembleEmpirical Mode Decomposition(EEMD)
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