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Signal-noise separation of sensor signal based on variational mode decomposition

机译:基于变分模式分解的传感器信号信噪分离

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For the problem of strong noisy interference in the data acquisition of the micro electro mechanical system (MEMS) hydrophone, signal-noise separation of the MEMS hydrophone receiving signal make use of the variational mode decomposition (VMD), which is a completely non-recursive variational method. The frequency of the signal is segmented by the default parameters, then the center frequency and the intrinsic mode function (IMF) are obtained. Firstly, the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the VMD are regarded as a bandpass filter, respectively. And the simulation signal of different signal-to-noise ratio is going to be denoised. According to the denoising effect and performance index, the experiment shows that the denoising effect of the VMD is much better than the previous two methods. Secondly, the VMD is applied to the Fenji experimental data of the North University of China in the lake trial. Finally, the results show that the VMD is one of the best estimation algorithms of MEMS hydrophone original signal.
机译:对于微机电系统(MEMS)水听器的数据采集中的强噪声干扰问题,MEMS水听器接收信号的信噪分离利用了变分分解(VMD),这是完全非递归的变分法。通过默认参数对信号的频率进行分段,然后获得中心频率和本征模式函数(IMF)。首先,将经验模态分解(EMD),整体经验模态分解(EEMD)和VMD分别视为带通滤波器。并且将对具有不同信噪比的模拟信号进行去噪。根据去噪效果和性能指标,实验表明,VMD的去噪效果远优于前两种方法。其次,将VMD方法应用于中北大学的Fenji实验数据中。最后,结果表明VMD是MEMS水听器原始信号的最佳估计算法之一。

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