Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.

中文翻译：

---

基于地基微波干涉法的桥梁动态挠度形态学辅助极点对称模式分解（MF-ESMD）去噪方法

桥梁动力挠度是结构安全检测的重要指标。地基微波干涉法具有非接触式测量和精度高的优点，因此广泛用于桥梁动态变形监测。然而，由于各种因素的影响，在通过地面微波干涉法获得的桥梁的动态挠度中存在许多噪声。为了减少噪声对地基微波干涉法获得的桥梁动态挠度的影响，本文提出了一种形态学滤波辅助的极点对称模式分解（MF-ESMD），用于地基微波获得的桥梁动态挠度信号去噪。微波干涉仪。第一，用地面微波干涉法获得的原始桥梁动态挠度被分解，以采用ESMD方法获得一系列本征模式函数（IMF）。其次，根据Spearman的rho算法去除了噪声占主导的IMF，并将其他分解后的IMF重建为新信号。最后，使用形态学滤波方法进一步消除了重构信号中的残留噪声。仿真和现场实验结果表明，所提出的MF-ESMD方法具有强大的信号去噪能力。使用形态学滤波方法进一步消除了重构信号中的残留噪声。仿真和现场实验结果表明，所提出的MF-ESMD方法具有强大的信号去噪能力。使用形态学滤波方法进一步消除了重构信号中的残留噪声。仿真和现场实验结果表明，所提出的MF-ESMD方法具有强大的信号去噪能力。