| 引用本文: | 朱颖,赵欣欣,孙大奇,郭辉.北斗卫星监测大跨桥梁基础变位算法[J].哈尔滨工业大学学报,2021,53(2):168.DOI:10.11918/202005045 |
| ZHU Ying,ZHAO Xinxin,SUN Daqi,GUO Hui.An algorithm for foundation displacement of long-span bridges based on Beidou navigation satellite system[J].Journal of Harbin Institute of Technology,2021,53(2):168.DOI:10.11918/202005045 |
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| 北斗卫星监测大跨桥梁基础变位算法 |
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朱颖1,2,赵欣欣1,2,孙大奇1,2,3,郭辉1,2
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(1.中国铁道科学研究院集团有限公司 铁道建筑研究所,北京 100081; 2.高速铁路轨道技术国家重点实验室 (中国铁道科学研究院),北京 100081; 3.中国铁道科学研究院,北京 100081)
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| 摘要: |
| 为实现北斗卫星定位系统（BDS）对大跨桥梁基础变位的连续监测和最终沉降量预测,需要消除卫星信号采集过程中的各种误差影响.本文提出通过平稳小波变换消除卫星信号中的多径误差；并将随机噪声简化成高斯白噪声模型,结合自相关函数噪声判定准测和经验模态分解降噪方法,降低信号中的随机噪声成分.为满足大跨桥塔沉降观测精度需求,采用双曲线模型定义北斗卫星监测数据和精密水准方法测量的桥塔沉降数据；以施工过程数据和模型化沉降数据作为输入层,通过BP神经网络建立不同数据间的非线性映射关系,实现北斗卫星实时测量数据的纠偏处理.最后,以某在建超千米公铁两用斜拉桥基础承台建立的北斗卫星沉降测点采集数据为例,根据3倍中位差原则去除北斗卫星采集数据的坏点,采用线性插值补偿丢失数据,通过本文方法降噪和纠偏后得到大跨桥梁基础承台沉降实时数据.与精密水准测量结果比较,本文方法具有亚毫米精度,满足大型桥塔结构沉降的连续观测和最终沉降量预测要求. |
| 关键词: 北斗卫星信号 信号降噪 基础沉降 平稳小波变换 BP神经网络 |
| DOI：10.11918/202005045 |
| 分类号:TU445 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(71942006)； 国铁集团系统性重大课题(P2018G002)； 中国铁道科学研究院基金项目(2018YJ048) |
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| An algorithm for foundation displacement of long-span bridges based on Beidou navigation satellite system |
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ZHU Ying1,2,ZHAO Xinxin1,2,SUN Daqi1,2,3,GUO Hui1,2
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(1.Railway Engineering Research Institute, China Academy of Railway Sciences Co. Ltd., Beijing 100081, China;2.State Key Laboratory for Track Technology of High-Speed Railway (China Academy of Railway Sciences), Beijing 100081, China; 3. China Academy of Railway Sciences, Beijing 100081, China)
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| Abstract: |
| To realize the continuous monitoring of foundation displacement and the prediction of final settlement of long-span bridges based on Beidou navigation satellite system (BDS), the influences of various errors should be eliminated in the collection process of satellite signals. This paper proposes a method to eliminate the multipath errors in BDS signals using stationary wavelet transform. The method simplifies random noises to Gaussian white noises, and combines the determinant criterion of noises and empirical mode decomposition (EMD) method to reduce the random noises in the signal based on autocorrelation function. In order to meet the observation accuracy for foundation displacement of long-span bridges, the measured BDS data and the settlement data obtained by precise levelling were defined by hyperbolic model. Based on the measured data during the construction process and the modelled settlement data, the non-linear relationship of the data was established by BP neural network to realize real-time correction of measured BDS data. The foundation pile of a rail-cum-road bridge under construction was taken as an example, which is over 1 km in length. The BDS data was collected, the bad points were eliminated, and the lost data was compensated by linear interpolation. The real-time measured data of the long-span bridge at foundation pile was denoised and corrected by the proposed method. Results show that compared with the precise levelling results, the proposed method had high precision, which could meet the requirements of continuous monitoring and final settlement prediction of bridge towers of long-span bridges. |
| Key words: BDS signals signal denoising foundation settlement stationary wavelet transform BP neural network |
