论文推荐|宁津生院士：基于卫星加速度恢复地球重力场的去相关滤波法

【摘要】 基于加速度法恢复地球重力场时，卫星加速度是由卫星轨道数值微分得到，而数值微分会放大高频误差，进而降低了重力场解算结果的精度。针对数值微分导出的加速度误差具有有色噪声的特性，本文提出利用去相关算法构造白化滤波器对加速度有色噪声进行滤波处理，并根据去相关的基本原理分别构造了基于三点差分和ARMA模型的白化滤波器。采用不同噪声背景的CHAMP卫星模拟轨道数据进行解算，结果表明：基于去相关滤波解算的重力场模型精度均要比等权解算的重力场模型精度高，初步验证了去相关滤波方法的有效性。

Abstract：n the acceleration approach for recovery of the Earth’s gravity field, the observation of satellite accelerations are derived from the satellite orbit data by numerical differentiation. However, the differentiation will rapidly amplify the high-frequency noise, which will degrade the accuracy of recovered gravity field model. Because the noise in the orbit-derived acceleration data is colored, whitening filters based on decorrelation technique are introduced to suppress the noise. Two whitening filters are constructed based on 3-points differential scheme and on ARMA model, respectively. As a test, simulated CHAMP orbit data with different type of noises are used to recover the gravity field model. The results demonstrate that the gravity field models recovered from the decorrelation filtering methods have higher accuracy than those from equal weight method, which validate the effectiveness of the decorrelation filtering methods.

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