基于GWO-VMD-SVD的Φ-OTDR信号降噪方法

Φ-OTDR Signal Denoising Method Based on GWO-VMD-SVD

摘要:

针对Φ-OTDR系统采集的信号中包含大量随机噪声的问题,提出了一种基于灰狼优化算法的变分模态分解联合奇异值分解的新型降噪方法(GWO-VMD-SVD)。通过灰狼优化算法寻找VMD分解中最优的分解层数K和二次惩罚因子α,抑制了模态混叠现象；引入排列熵判定机制区分有用信号分量和噪声分量；将有用信号分量保留,同时对噪声分量使用SVD分解进行二次降噪,提取其中的有用信号；将两次降噪保留的有用信号进行重构,得到降噪后的信号。实验结果表明,该方法相对于VMD-PE和EEMD-CC,信噪比更高,能更有效地保留信号中的有用信息。

Abstract:

To solve the problem that the signals collected by Φ-OTDR system contain a lot of random noise, a new noise reduction method based on grey Wolf optimization algorithm with variational mode decomposition combined with singular value decomposition (GWO-VMD-SVD) is proposed. The gray Wolf optimization algorithm was used to find the optimal decomposition layers and the quadratic penalty factor in VMD decomposition, and the mode aliasing phenomenon was suppressed. The permutation entropy determination mechanism was introduced to distinguish the useful signal component from the noise component. The useful signal component was retained, and the noise component was denoised by SVD decomposition to extract the useful signal. The useful signal retained by two denoising was reconstructed and the denoised signal was obtained. Experimental results show that the proposed method has higher SNR than VMD-PE and EEMD-CC, and can retain useful information in the signal more effectively.