北京测绘2025，Vol.39Issue(5)：748-754,7.DOI:10.19580/j.cnki.1007-3000.2025.05.028
基于CEEMDAN-PE-SSA模型的特长钢箱梁桥GNSS监测数据降噪
Noise reduction of GNSS monitoring data for ultra-long steel box girder bridges based on CEEMDAN-PE-SSA model
摘要
Abstract
This paper addressed the complex problem of multipath effects and random noise in global navigation satellite system(GNSS)monitoring data of ultra-long steel box girder bridges.A new combined noise reduction model is proposed,based on the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method,integrated with permutation entropy(PE)theory and singular spectrum analysis(SSA)method.The main steps of the noise reduction process are as follows:First,the GNSS monitoring data of the ultra-long steel box girder bridge are decomposed into several intrinsic mode functions(IMF)using the CEEMDAN method.Then,the IMF components are separated into different frequency bands based on their PE values,and the SSA method is used to reduce noise in the high-frequency components.Finally,the different components and the residual are reconstructed.To verify the noise reduction effect of the combined model,experiments were conducted using both simulated signals and GNSS monitoring data from the ultra-long steel box girder bridge.The results indicate that compared to the standalone CEEMDAN method and SSA method,the combined model significantly improves the noise reduction effect.Additionally,based on spectral analysis,this paper extracts the deformation amount,trend components,and high-frequency noise components that contain vibration information from the GNSS monitoring data.关键词
特长钢箱梁桥/全球导航卫星系统(GNSS)监测数据/自适应噪声的完备集合经验模态分解/样本熵/奇异谱分析(SSA)Key words
ultra-long steel box girder bridge/global navigation satellite system(GNSS)monitoring data/complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)/sample entropy(SE)/singular spectrum analysis(SSA)分类
天文与地球科学引用本文复制引用
李强,刘超..基于CEEMDAN-PE-SSA模型的特长钢箱梁桥GNSS监测数据降噪[J].北京测绘,2025,39(5):748-754,7.基金项目
浙江省自然科学基金(LTGG23D010001) （LTGG23D010001）
