摘要
对地面沉降监测数据序列进行滤波与预测是地面沉降数据分析的重要内容,但传统方法是针对单个沉降点进行处理,无法进行区域整体分析,为此将时空Kalman滤波应用于GPS地面沉降数据处理,并根据GPS地面沉降监测数据特点,重点探讨了地面沉降时空Kalman滤波的空间场确定方法以及迭代滤波算法。模拟实验及天津CORS站沉降数据分析结果表明,时空Kalman滤波比单点Kalman滤波精度更高,且更稳健,同时由于其可以同时在时间域和空间域进行滤波与预测,因而实现了地面沉降数据的整体分析。
Filtering and predicting the land subsidence monitoring data series is an important content of land subsidence monitoring data analysis. But the traditional methods are mostly targeted at single settlement point, so an overall analysis of the section is unavailable. According to the characteristic of GPS land subsidence monitoring data, this paper mainly discusses the determination of the Kriging space fields of space-time Kalman filter and the iterative filtering algorithm. Simulation experiment and land subsidence monitoring data analysis of Tianjin CORS show that space-time Kalman filter are more accurate and robust than single-point Kalman Filter. Meanwhile, this method allows predictions in time as well as in space, so an overall analysis of land subsidence monitoring data is achieved.
引文
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