扬州大学 物理科学与技术学院,江苏,扬州,225009
纸质出版:2026
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单佳杰, 夏长权. 基于EMD与改进小波阈值法的传感器信号降噪模型[J]. 传感器与微系统, 2026,45(3):134-139.
SHAN Jiajie, XIA Changquan. Sensor signal denoising model based on EMD and improved wavelet threshold method[J]. 2026, 45(3): 134-139.
单佳杰, 夏长权. 基于EMD与改进小波阈值法的传感器信号降噪模型[J]. 传感器与微系统, 2026,45(3):134-139. DOI:
SHAN Jiajie, XIA Changquan. Sensor signal denoising model based on EMD and improved wavelet threshold method[J]. 2026, 45(3): 134-139. DOI:
针对传感器在工作过程中易受内外部影响产生噪声的问题,提出了一种基于经验模态分解(EMD)与小波阈值法的信号降噪模型。首先
对信号进行EMD得到若干本征模态函数(IMF);其次
利用方差比区分噪声分量和信号分量,将不同分量进行数据平滑处理并重构;最后
将重构信号使用改进小波阈值法进行降噪处理。仿真实验中,该模型的信噪比(SNR)为29.888,均方根误差(RMSE)为0.119,优于其他对比算法。气体传感器实际测试结果表明:该模型可以有效降低高频噪声,降噪效果优于其他对比算法。
Aiming at the problem that sensors are susceptible to internal and external influence during operation
which generates noise
a signal denoising model based on empirical mode decomposition(EMD)and wavelet threshold method is proposed.Firstly
the signal is decomposed by empirical mode decomposition to obtain several intrinsic mode functions(IMFs).Secondly
the variance ratio is used to distinguish the noise component and the signal component
and the data of different components are smoothed and reconstructed.Finally
the reconstructed signal is denoised by the improved wavelet threshold method.In simulation experiment
the signal-to-noise ratio(SNR)of the proposed model is 29.888
and the root mean square error(RMSE)is 0.119
which is superior to other comparison algorithms.The actual test result shows that the model can effectively reduce high-frequency noise
and the noise reduction effect is superior to other comparison algorithms.
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