How to deal with colored noises of GOCE(Gravity field and steady-state Ocean Circulation Explorer) satellite has been the key to data processing.This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis.According to the analysis results,gravity field model of the optimal degrees 90-240 is given,which is recovered by GOCE gradient data.This paper presents an iterative Wiener filtering method based on the gravity gradient invariants.By this method a degree-220 model was calculated from GOCE SGG(Satellite Gravity Gradient) data.The degrees above 90 of ITG2010 were taken as the prior gravity field model,replacing the low degree gravity field model calculated by GOCE orbit data.GOCE gradient colored noises was processed by Wiener filtering.Finally by Wiener filtering iterative calculation,the gravity field model was restored by space-wise harmonic analysis method.The results show that the model’s accuracy matched well with the ESA’s(European Space Agency) results by using the same data.
How to deal with colored noises of GOCE (Gravity field and steady - state Ocean Circulation Explorer) satellite has been the key to data processing. This paper focused on colored noises of GOCE gradient data and the frequency spectrum analysis. According to the analysis results, gravity field model of the optima] degrees 90-240 is given, which is recovered by COCE gradient data. This paper presents an iterative Wiener filtering method based on the gravity gradient invariants. By this method a degree-220 model was calculated from GOCE SGG (Satellite Gravity Gradient) data. The degrees above 90 of ITG2010 were taken as the prior gravity field model, replacing the low degree gravity field model calculated by GOCE orbit data. GOCE gradient colored noises was processed by Wiener filtering. Finally by Wiener filtering iterative calculation, the gravity field model was restored by space-wise harmonic analysis method. The results show that the model's accuracy matched well with the ESA's (European Space Agency) results by using the same data,