Quality assessment for terrestrial gravity anomalies by variance component estimation using GOCE gradiometric data and Earth’s gravity models

Abstract Satellite gradiometry data provide the framework for estimating and validating Earth's gravity field from second and third order derivatives of the Earth's gravitational potential. Such procedures are especially useful when applied locally, as they relate to local and regional characteristics of the real gravity field. In the present study a joint inversion procedure is proposed for the estimation of gravity anomalies at sea surface level from second and third order potential derivatives, based on a standard Gauss-Markov estimation model. The estimation procedure is applied for a test area stretching over Iran involving simulated grids from GOCE-only model GGM_TIM_R05 at GOCE altitude and gravity anomalies recovered at sea level. In order to validate the proposed estimation three different reductions have been considered independently, namely the removal of the long-wavelength part of the observed field through a global gravity model, the removal of the high-frequency part of the field through the incorporation of a topographic/isostatic gravity model and the application of variance component estimation. The application of a global gravity model leads to an improvement in the individual component estimation of the order of magnitude 3 per cent to 73 per cent, with a significant reduction in bias to 4 mGal. Smoother gradient components can come out according to removing the topography and taking into account for isostasy that improved up results of recovery to 25 per cent for the radial second order derivative. Finally, the implementation of variance component estimation leads to no significant improvement in results of recovered gravity anomalies.

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GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

Journal of Navigation ◽

10.1017/s0373463321000564 ◽

2021 ◽

pp. 1-16

Author(s):

Hong Hu ◽

Xuefeng Xie ◽

Jingxiang Gao ◽

Shuanggen Jin ◽

Peng Jiang

Keyword(s):

Stochastic Model ◽

Least Squares ◽

Stochastic Models ◽

Variance Component ◽

Satellite System ◽

Variance Component Estimation ◽

Short Baseline ◽

Component Estimation ◽

Zero Baseline ◽

Global Navigation Satellite

Abstract Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.

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