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Wavelet De-noising of GNSS Based Bridge Health Monitoring Data

机译:基于GNSS的桥梁健康监测数据的小波去噪。

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GNSS signal multipath occurs when the GNSS signal reflects off objects in the antenna environment and arrives at the antenna via multiple paths. A bridge environment is one that is prone to multipath with the bridge structure, as well as passing vehicles providing static and dynamic sources of multipath. In this paper, the Wavelet Transform (WT) is applied to bridge data collected on the Machang cable stayed bridge in Korea. The WT algorithm was applied to the GNSS derived bridge deflection data at the mid-span. Up to 41% improvement in RMS was observed after wavelet shrinkage de-noising was applied. Application of this algorithm to the torsion data showed significant improvement with the residual average and RMS decreased by 40% and 45% respectively. This method enabled the generation of more accurate information for bridge health monitoring systems in terms of the analysis of frequency, mode shape and three dimensional deflections.
机译:GNSS信号多径发生在GNSS信号反射掉天线环境中的物体并通过多条路径到达天线时。桥梁环境是一种易于使用桥梁结构进行多路径操作的环境,并且通过的车辆会提供静态和动态的多路径源。在本文中,小波变换(WT)被应用于韩国Machang斜拉桥上收集的桥数据。将WT算法应用于GNSS导出的中跨桥挠度数据。应用小波收缩去噪后,RMS最高可提高41%。该算法在扭转数据上的应用显示出显着的改进,残余平均值和RMS分别降低了40%和45%。通过分析频率,振型和三维挠度,该方法可以为桥梁健康监测系统生成更准确的信息。

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