基于EMD-CF的级联光栅微振动传感器光谱优化算法

基于级联光栅的微振动传感器是一种典型的微振动信号测量方案，然而由于光信号在级联光栅中经过多次透射和反射，导致光谱信噪比差、成分复杂等问题。基于此，文中提出一种结合经验模态分解和切比雪夫滤波技术的光谱信号优化算法。首先，将传感器原始光谱通过经验模态分解得到一系列本征模函数；其次，利用所提出的自适应滤波方法，确定包含反射峰成分的本征模函数阶数，并对其进行切比雪夫低通滤波；最后，将滤波器输出进行重构，即得到优化后的传感器光谱。使用振幅为±8 mV、频率为500 Hz的微振动激励信号进行实验验证。结果表明:文中所提出算法可以较好地还原激励源发出的微振动信号，相比传统方法精度提高87.5%以上。

Spectrum optimization algorithm of cascaded grating micro-vibration sensor based on EMD-CF

The micro-vibration sensor based on cascaded grating is a typical micro-vibration signal measurement scheme. However, due to the multiple transmission and reflection of optical signal in the cascaded grating, the sensor is subject to the poor spectral signal-to-noise ratio and complex components. Based on this, a spectrum signal optimization algorithm combined with empirical mode decomposition and chebyshev filter was proposed in this paper. Firstly, the original spectrum of the sensor was decomposed into a series of intrinsic eigenmode functions by empirical mode decomposition; Secondly, the order of the intrinsic mode functions including the reflection peak component was determined by using the proposed adaptive filtering method, and the chebyshev low-pass filtering was performed on these orders; Finally, the optimized sensor spectrum was obtained by reconstructing the output of the filter. A micro-vibration excitation signal with an amplitude of ±8 mV and frequency of 500 Hz was used for experimental verification. The results show that the proposed algorithm can effectively restore the micro-vibration signal from the excitation source, and the accuracy is improved by more than 87.5% compared with the traditional methods.