| 摘要: |
| 为了增强未知样式信号的信噪比估计性能，提出了一种基于经验模态分解（EMD）
的信号信噪比估计新算法，通过固有模态函数（IMF）分量平均周期判断信号与噪声界限。
给出了经验模态分解估计法的工作原理和流程图，分析了经验模态分解估计法的性能。仿真
结果表明，与信号空间分解法一样，经验模态分解估计法能够实现盲信号信噪比估计，后者
估计均方误差比前者要小，在0 dB信噪比下均方误差不超过0.3 dB。 |
| 关键词: 信噪比估计 盲估计 经验模态分解 子空间分解 |
| DOI： |
|
| 基金项目:解放军电子工程学院博士创新基金资助项目 |
|
| A novel blind SNR estimation algorithm based on empirical mode decomposition |
| LI Guo-han,WANG Ke-ren,ZHANG Song |
| () |
| Abstract: |
| To enhance the performance Signal-to-Noise ratio(SNR) e
stimation of unknown type signals, a n
ovel algorithm based on Empirical Mode Decomposition(EMD) is proposed, in which
the boundary of the signal and noise is determined by the average period of Int
rinsic Mode Functions (IMF). The principle and flow chart of the algorithm are p
resented, an
d the EMD-based method performance is also analysed. Simulation results show tha
t,like the method based on signal subspace decomposition, EMD-based metho
d can adapt to unknown signals SNR estimation, the estimated Mean Square Error(M
SE)
of EMD-based method is smaller than the former, and it is not more than 0.3 d
B when the SNR is 0 dB. |
| Key words: SNR estimation blind estimation empirical mode decomposition (EMD) subspace deco
mposition |
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