【摘要】 针对滑坡深部变形监测中MEMS传感器信号存在的随机噪声问题,提出了一种基于粒子群优化(PSO)算法改进的小波阈值去噪方法。方法通过引入PSO算法优化改进型软硬阈值折衷函数中的关键参数,实现对监测信号的有效去噪。改进后的算法能够抑制信号中的随机噪声,且能更好地保留信号的细节信息,提高信号的质量和可靠性。与传统的硬阈值和软阈值去噪方法相比,所提方法在信噪比上有显著提升,达到了原信噪比的两倍以上,同时均方根误差也得到了明显降低,表明该方法能够更精确地恢复信号。通过对选定的bior3.3小波基函数的试验分析,进一步验证了该方法在滑坡深部变形监测中的优越性和实际应用价值。研究结果表明,该方法不仅具有较强的噪声抑制能力,还能够保留更多的信号细节,适用于滑坡等地质灾害监测领域,为MEMS传感器信号处理提供了新的思路,具有较强的普适性和推广价值。更多还原

【Abstract】 Aiming at the random noise problem of MEMS sensor signal in landslide deep deformation monitoring,an improved wavelet threshold denoising method based on particle swarm optimization(PSO) algorithm is proposed. By introducing PSO algorithm to optimize the key parameters in the improved soft and hard threshold compromise function,the effective denoising of the monitoring signal is realized. The improved algorithm can suppress the random noise in the signal,and can better retain the detailed information of the signal,improve the quality and reliability of the signal. Compared with the traditional hard threshold and soft threshold denoising methods,the proposed method has a significant improvement in signal-to-noise ratio,reaching more than twice the original signal-to-noise ratio,and the root mean square error has also been significantly reduced,indicating that the method can recover the signal more accurately. Through the experimental analysis of the selected bior3. 3 wavelet basis function,the superiority and practical application value of this method in the deep deformation monitoring of landslide are further verified. The research results show that this method not only has strong noise suppression ability,but also can retain more signal details,which is suitable for the field of geological disaster monitoring such as landslide. It provides a new idea for MEMS sensor signal processing,and has strong universality and promotion value.更多还原