基于L-T的联合算法在光纤法珀纳米压力传感器脉压检测信号中的应用

摘要：

摘要： 研究了一种改进的去噪方法及其在脉冲拍频信号去噪中的应用。 该算法结合了局部均值分解(Local mean decomposition, LMD)和时频峰值滤波(Time-frequency peak filtering，TFPF)的优点， 称为L-T算法。 TFPF作为一种经典的时频滤波方法， 较长的窗长可以在保留信号幅值的前提下有效抑制随机噪声， 而较短的窗长则导致信号幅值严重衰减。 因此， 为了保持有效信号幅度、抑制随机噪声， 对LMD和TFPF进行了改进。 首先利用LMD将原始信号分解为无级生存（Progression-free survival, PFS）， 然后计算各乘积函数均值的标准误差， 将许多PFSs分为有用分量、混合分量和噪声分量。 其次， 将短窗TFPF用于有用分量去噪， 长窗TFPF用于混合分量去噪， 得到重构后的信号。 最后， 将该算法用于F-P压力传感器的降噪。 实验结果表明， 与传统小波去噪算法相比， L-T算法去噪效果更优。

Abstract： An improved denoising method and its application in pulse beat signal denoising are studied. The proposed denoising algorithm takes the advantages of local mean decomposition (LMD）and time-frequency peak filtering (TFPF), called L-T algorithm. As a classical time-frequency filtering method, TFPF can effectively suppress random noise with signal amplitude retained when selecting a longer window length, while the signal amplitude will be seriously attenuated when selecting a shorter window length. In order to maintain effective signal amplitude and suppress random noise, LMD and TFPF are improved. Firstly, the original signal is decomposed into progression-free survival （PFS）by LMD, and then the standard error of mean (SEM) of each product function is calculated to classify many PFSs into useful component, mixed component and noise component. Secondly, by using the shorter window TFPF for useful component and the longer window TFPF for mixed component, noise component is removed and the final signal is obtained after reconstruction. Finally, the proposed algorithm is used for noise reduction of an Fabry-Perot (F-P) pressure sensor. Experimental results show that compared with traditional wavelet, L-T algorithm has better denoising effect on sampled data.