基于辅助传感器阵列与NECNN-BiLSTM深度神经网络的磁场信号去噪方法研究  

Magnetic field signal denoising based on auxiliary sensor array and NECNN-BiLSTM deep neural networks

作　　者：胡书正 王骁贤 宋俊材 陆思良[1] Hu Shuzheng;Wang Xiaoxian;Song Juncai;Lu Siliang(College of Electrical Engineering and Automation,Anhui University,Hefei 230601,China;College of Electronics and Information Engineering,Anhui University,Hefei 230601,China;College of Internet,Anhui University,Hefei 230601,China)

机构地区：[1]安徽大学电气工程与自动化学院,合肥230601 [2]安徽大学电子信息工程学院,合肥230601 [3]安徽大学互联网学院,合肥230601

出　　处：《仪器仪表学报》2024年第7期227-238,共12页Chinese Journal of Scientific Instrument

基　　金：安徽省自然科学基金(2308085Y03);国家自然科学基金(62203010)项目资助。

摘　　要：针对强噪声干扰下磁场信号精准去噪难题,提出一种结合中心-卫星架构辅助传感器阵列和深度噪声重建网络的磁场信号去噪新方法。首先,搭建磁场传感器阵列,通过有限元分析进行传感器阵列位置优化,分析中心和卫星传感器信号之间的信号特征。随后,构造一种结合噪声增强卷积神经网络(NECNN)和双向长短期记忆网络(BiLSTM)的深度神经网络模型,利用传感器阵列捕获的噪声信号对构造的网络模型进行训练,揭示中心传感器信号和卫星传感器信号之间的非线性映射关系。最后,在磁场检测过程中,利用卫星传感器阵列噪声重建出中心传感器的噪声分量,再将中心传感器捕获的含噪信号减去重建噪声,得到去噪后的待检测磁场信号。实验结果表明,本文提出方法在磁场去噪的最大误差与均方根误差指标上均优于常规方法,为磁场强干扰下信号动态去噪提供一种新手段,有望应用于电流检测、磁场成像、电池质量检测等领域。Aiming at the problem of accurate denoising of magnetic field signals under strong noise interference,a new method of denoising magnetic field signals combining the auxiliary sensor array of center-satellite architecture and the deep noise reconstruction network is proposed.First,the magnetic field sensor array is built,and finite element analysis is used to optimize the sensor array positions and analyze the signal characteristics between the center and satellite sensors.Subsequently,a deep neural network model combining noise-enhanced convolutional neural network(NECNN)and bi-directional long short-term memory(BiLSTM)is constructed.The model is trained using the noise signals captured by the sensor array to reveal the nonlinear mapping relationship between the center sensor signal and the satellite sensor signal.Finally,in the magnetic field detection process,the noise components of the center sensor are reconstructed using the noise of the satellite sensor array.The denoised magnetic field signal is obtained by subtracting the reconstructed noise from the noisy signal captured by the center sensor.The experimental results show that the proposed method outperforms the conventional method in terms of the maximum error and the root mean square error index of magnetic field denoising.This new approach provides a new means of dynamic denoising of signals under strong magnetic field interference,and is expected to be applied in the fields of current detection,magnetic field imaging,and battery quality detection.

关 键 词：磁场传感器阵列 磁场信号去噪 噪声重建 深度神经网络 

分 类 号：TH7[机械工程—仪器科学与技术]