基于集合经验模态的低SNR下信源数估计方法  

Source Enumeration Method Based on Ensemble Empirical Mode Decomposition under Low SNR

作　　者：胡耀敏 潘晴[1] 田妮莉[1] HU Yao-min;PAN Qing;TIAN Ni-li(College of Information Engineering,Guangdong University of Technology,Guangzhou Guangdong 510006,China)

机构地区：[1]广东工业大学信息工程学院,广东广州510006

出　　处：《计算机仿真》2022年第9期186-189,207,共5页Computer Simulation

基　　金：国家自然科学基金项目(61901123)。

摘　　要：针对经验模态分解在提取阵列信号特征时对噪声非常敏感和过低信噪比会使模式混叠更加复杂的问题,提出了一种基于集合经验模态分解和BP神经网络的信源个数估计方法。所提方法在经验模态分解中加入均值为零的高斯白噪声,通过白噪声的频谱均匀分布的特性,使不同时间尺度的信号分量自动映射到相应的IMF上,将提取到的阵列信号瞬时相位特征放入BP神经网络中训练,得到能进行信源个数估计的分类器模型。最后,射频消音室实验证明,即使在低信噪比和信源总数少于或仅少于天线总数一个的极端情况下,所提方法也能获得良好的检测性能。Empirical mode decomposition(EMD) is very sensitive to noise and the low signal-to-noise ratio makes mode aliasing more complex when extracting array signal features, a method of source number enumeration based on ensemble EMD and BP neural network is proposed. In the proposed method, Gaussian white noise with zero mean value is added to the EMD,and the signal components of different time scales are automatically mapped to the corresponding IMF through the spectrum uniform distribution of white noise. The extracted instantaneous phase characteristics of the array signal are put into BP neural network for training, and the classifier model which can estimate the number of sources is obtained. Finally, RF anechoic chamber experiments show that the proposed method can achieve good detection performance even in the extreme cases of low SNR and the total number of sources is less than or only less than one antenna.

关 键 词：信源数估计 经验模态分解 模式混叠 集合经验模态分解 

分 类 号：TN955[电子电信—信号与信息处理]