| GOCE卫星重力梯度数据反演重力场的滤波器设计与比较分析 |
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| 引用本文： | 刘滔,钟波,李贤炮,谭江涛. GOCE卫星重力梯度数据反演重力场的滤波器设计与比较分析[J]. 武汉大学学报(信息科学版), 2023, 48(5): 694-701. DOI: 10.13203/j.whugis20200705 |
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| 作者姓名： | 刘滔 钟波 李贤炮 谭江涛 |
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| 作者单位： | 1.武汉大学测绘学院, 湖北 武汉, 430079 |
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| 基金项目： | 国家重点研发计划2018YFC1503503国家自然科学基金41974015国家自然科学基金42061134007 |
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| 摘 要： | 地球重力场和海洋环流探测（gravity field and steady-state ocean circulation explorer,GOCE）卫星重力梯度数据有色噪声和低频系统误差的滤波处理是反演高精度地球重力场的一个关键问题。针对GOCE卫星重力梯度数据的滤波处理，基于移动平均（moving average,MA）方法和CPR(circle per revolution)经验参数方法设计了两类低频系统误差滤波器，并分别将这两类滤波器与基于自回归移动平均（auto-regressive and moving average,ARMA）模型设计的有色噪声滤波器组合起来形成级联滤波器。为了分析滤波器处理的实际效果，基于空域最小二乘法采用70 d的GOCE观测数据，并联合重力恢复与气候实验（gravity recovery and climate experiment,GRACE）数据分别反演了224阶次的重力场模型GOGR-MA(MA+ARMA级联滤波)和GOGR-CPR(CPR+ARMA级联滤波)。将反演模型与采用同期数据求解的第一代GOCE系列模型及GOCE和GRACE联合模...
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| 关 键 词： | 卫星重力梯度 有色噪声 低频系统误差 滤波器设计 地球重力场和海洋环流探测重力场反演 |
| 收稿时间： | 2021-04-07 |
Design and Comparison of Filters of Gravity Field Inversion from GOCE Satellite Gravity Gradient Data |
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| Affiliation: | 1.School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China2.Key Laboratory of Geospatial Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan 430079, China |
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| Abstract: | Objectives The filtering of colored noise and low-frequency systematic errors of gravity gradient data from gravity field and steady-state ocean circulation explorer (GOCE) satellite is a key issue in the inversion of high-precision Earth gravity fields. Methods For the filtering of GOCE satellite gravity gradient data, we design two kinds of low-frequency systematic error filters based on moving average (MA) method and circle per revolution (CPR) empirical parameter method, and then combine them with the colored noise filter based on auto-regressive moving average (ARMA) model to form cascaded filters. To analyze the actual effect of these filters, the gravity field models GOGR-MA (MA+ARMA cascade filtering) and GOGR-CPR (CPR+ARMA cascade filtering) gravity field models of order 224 are inversed by using 70 d of GOCE data based on the space-wise least square method, respectively. The inversion models are compared with the first generation of GOCE series models, and GOCE and GRACE combined model GOCO01S solved by contemporaneous data. Then the model is checked by high-precision global positioning system leveling data and mean dynamic topography model. Results The results show that both the MA filter and CPR empirical parameter filter can weaken the influence of low-frequency system errors, and the latter is better. Moreover, ARMA decorrelation filter can effectively whiten the colored noise of gravity gradient data. The combined models GOGR-MA and GOGR-CPR have similar accuracy and they perform better than GOCO01S model developed by European Space Agency (ESA) using the same period data. Conclusions The inversion results verify the correctness and effectiveness of the proposed cascaded filters, which can provide a reference for filtering of GOCE gravity gradient data and inversion of high-precision gravity field. |
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