复杂环境下结合EMD的GPS-IR水位反演方法
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1.南京信息工程大学遥感与测绘工程学院;2.华设设计集团股份有限公司

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(国家自然科学基金,41704024)


GPS-IR water level retrieval method combined with EMD in complex environment
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1.School of Remote Sensing &2.Geomatics Engineering,Nanjing University of Information Science &3.Technology;4.China Design Group Co.,ltd

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    摘要:

    本文利用BRST港口和英国塞文大桥监测系统GNSS双频观测数据,分别在静态和高动态环境下进行GPS-IR水位反演。探究传统GNSS监测系统在静态、高动态场景下进行水位反演的可行性与精度。结果表明:L1波段反演精度高于L2波段;在静态场景下,GPS-IR水位反演结果与验潮站数据相关系数均达到0.98以上;在高动态场景下,桥梁GPS水位反演精度稍低。利用经验模态分解(Empirical Mode Decomposition,EMD)方法对算法进行改进,提高了在桥梁复杂环境下GPS-IR水位反演结果的精度,均方根误差(Root Mean Squared Error, RMSE)相比经典方法降低了约50%。提高了GPS-IR技术在不同水域环境下的适用性,结果证明GPS-IR技术在水位监测中具有很好的应用前景。

    Abstract:

    This paper uses the GNSS dual-frequency observation data of the BRST port and the Severn Bridge monitoring system in the United Kingdom to perform GPS-IR water level inversion in static and high dynamic environments, respectively. To explore the feasibility and accuracy of water level inversion by traditional GNSS monitoring system in static and high dynamic scenarios. The results show that the inversion accuracy of the L1 band is higher than that of the L2 band; in the static scene, the correlation coefficient between the GPS-IR water level inversion results and the tide gauge data is above 0.98; in the high dynamic scene, the retrieval accuracy of F001 station is slightly lower, using the Empirical Mode Decomposition (EMD) method to improve the algorithm, improve the accuracy of GPS-IR water level retrieval results in complex bridge environment, compared with the classical method, Root Mean Square Error is reduced by about 50%. Improve the applicability of GPS-IR technology in different water environments, the results show that GPS-IR technology has a good application prospect in water level monitoring.

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历史
  • 收稿日期:2022-06-10
  • 最后修改日期:2022-10-12
  • 录用日期:2022-11-07
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