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• This paper is the first to use the matrix pencil model to extract the main phase component of the interferometric phase in combination with a simple and effective adaptive window determination method. • This paper is the first to filter the residual phase by a fast NLM filtering method and then to combine the principal phase component obtained in the first step with the residual phase filtered in the second step, thereby revealing the final filtered phase. • This method combines the advantages of frequency domain filtering and spatial domain filtering, thereby obtaining superior filtering results. Interferometric phase filtering is a key step in InSAR data processing. However, when utilizing interferometric phase filtering methods, it is difficult to preserve useful phase information while eliminating as many residuals as possible. This paper proposes a novel two-step noise reduction approach for interferometric phase images. This method uses the amended matrix pencil model to extract the main phase component of the interferometric phase in combination with a simple and effective adaptive window determination method for the first time. Then, fast non-local means filtering is used to extract an accurate residual phase component. Finally, the principal phase component extracted in the first step is summed with the residual component from the second step to obtain the final result. Several common interferometric phase filtering methods are compared using two sets of simulated data and one set of real TanDEM-X InSAR data. The results show that the proposed method can not only preserve useful phase information but also effectively remove residuals. In addition, the proposed method boasts a superior filtering efficiency. [ABSTRACT FROM AUTHOR]

Abstract With the Global Navigation Satellite System (GNSS) developing, the single-frequency single-epoch multiple GNSSs (multi-GNSS) relative positioning has become feasible. Since a larger number of the observed satellites make the instantaneous (single-epoch) positioning time-consuming, a proper satellite selection is necessary. Among the present methods, the satellite selection with a fixed high cut-off elevation angle (CEA) is least time-consuming. However, there is no criterion how large a fixed high CEA should be to achieve a high success rate and less time consumption. Besides, a fixed high CEA makes the number of visible satellites largely variable, which affects the success rate. Hence, a satellite selection strategy based on ambiguity dilution of precision (ADOP) is proposed. Firstly, the theoretical proof that the ADOP increases the least when removing satellites are all low-elevation-angle satellites is given, which is important to achieve the fast positioning with a high success rate. Then, the threshold β is calculated for a different number of satellites and a given ADOP. The satellites are selected based on their elevation angles from high to low until β of the selected satellites becomes smaller than the corresponding threshold; this method is called the extended floating CEA multi-GNSS (EF-multi-GNSS). The comparison of the single-frequency single-epoch positioning performance of the EF-multi-GNSS with the satellite selections based on a fixed low CEA (L-multi-GNSS) and a fixed high CEA (H-multi-GNSS) via the relative positioning experiments shows that: (1) the EF-multi-GNSS with a minimal number of satellites can achieve the fast positioning and a high success rate close to 100%. It can greatly reduce the time consumption of the L-multi-GNSS, by about 64.0%, by selecting 12.6 satellites of 23.4 satellites; (2) the floating CEA of EF-multi-GNSS eliminates the consideration how large a fixed high CEA should be, and a CEA larger than the fixed high CEA of the H-multi-GNSS makes it more suitable for different conditions. [ABSTRACT FROM AUTHOR]

A gravitational field of an ore-bearing asteroid, which usually has an irregular shape and heterogeneous density, is a prerequisite for asteroid exploration (e.g., space mining) missions. Thus, it is a hot topic in planetary science to model the external gravitational field of the ore-bearing asteroid in an efficient and accurate way. In this study, a computational fluid dynamics (CFD)-based method proposed in our previsous studies is investigated further. Firstly, six types of density distribution in asteroid Bennu are simulated as six experimental cases; then the gravitational fields are derived by using the CFD-based method; and finally, the results are compared to the other two solutions derived from the mascons gravity model method (as a benchmark method) and polyhedron gravity model method, respectively. The CFD-based method shows a superior performance in modelling the gravitational field of an irregularly-shaped asteriod with heterogeneous density in terms of both accuracy and efficiency. For example, the CFD-based method only costs 340 s obtaining 1,650,000 gravitational vectors outside the asteroid with a relative error of 1.27 %, compared to the computation time of 14,520 s and the solution accuracy of 3.84 % for the polyhedron gravity model method on the same testing points. The comparison study demonstrates a good potential application of the CFD-based gravitational field modeling method in asteroid exploration missions. • A CFD method can solve the gravity field of asteroids with various density types. • A CFD method is more efficient in solving gravitational field than other methods. • The mass distribution of an asteroid can be reflected in the external gravity field. • The CFD method exhibits efficient, accurate and stable properties. [ABSTRACT FROM AUTHOR]

Abstract In GNSS applications, carrier-smoothed-code is a widely used technique to combine code pseudo-range and carrier phase measurements. A dynamical ionospheric delay modeling method is proposed based on Kalman filter and least-squares theory. The level of the process noise is adaptively tuned along with the real-time KF state estimation, based on the online variance component estimation method. Meanwhile, the correlations of the time differenced carrier phase measurements are considered. This approach avoids overly optimistically evaluating the estimate and improves the transient accuracy of the estimates. A real GPS dataset is employed to check the performance of the proposed method under different conditions. The results show that the new algorithm can model the ionospheric delay variation well with different sampling intervals or even in ionospheric abnormal environment. The positioning accuracy can be confirmed, about 21%, 35% and 16% better are obtained in the N, E, and U direction than raw dataset. [ABSTRACT FROM AUTHOR]

Abstract Precipitable water vapour (PWV) is a key factor for monitoring climate and the hydrological cycle. Here, PWV is calculated using 249 ground-based GNSS stations derived from the Crustal Movement Observation Network of China (CMONOC). Zenith total delay (ZTD) is estimated using the GAMIT/GLOBK (Ver. 10.4) and the zenith hydrostatic delay (ZHD) is calculated using the layered European Centre for Medium-Range Weather Forecasting (ECMWF) ERA-Interim data. The GNSS-derived ZTD is validated using the radiosonde and ECMWF data with the root mean square errors (RMSE) of 19.1 mm and 12.5 mm, respectively. The ECMWF-derived surface pressure (P s) used to calculate the ZHD and the weighted mean temperature (T m) calculated based on the layered ECMWF data are also evaluated using radiosonde data with RMSE values of 1.14 hPa and 1.24 K, respectively. Consequently, the final CMONOC-derived PWV at 249 stations is obtained and compared with radiosonde-derived and ECMWF-derived PWV while the RMSE values are 1.38 mm and 1.30 mm, respectively. The two-dimensional (2-d) PWV image derived from CMONOC is also compared with that from ECMWF, which shows good consistency across different seasons. Highlights • The PWV is obtained over the mainland of China with the accuracy of about 1.4 mm. • GNSS-derived ZTD is validated with that from radiosonde and ECMWF data. • Ps and Tm derived from ECMWF are also validated using the radiosonde data. • Two-dimensional PWV image derived from CMONOC shows nonuniform characteristics of water vapour over the China. [ABSTRACT FROM AUTHOR]

The symmetric Helmert transformation model is widely used in geospatial science and engineering. Using an analytical least-squares solution to the problem, a simple and approximate error analysis is developed. This error analysis follows the Pope procedure solving nonlinear problems, but no iteration is needed here. It is simple because it is not based on the direct and cumbersome error analysis of every single process involved in the analytical solution. It is approximate because it is valid only in the first-order approximation sense, or in other words, the error analysis is performed approximately on the tangent hyperplane at the estimates instead of the original nonlinear manifold of the observables. Though simple and approximate, this error analysis’s consistency is not sacrificed as can be validated by Monte Carlo experiments. So the practically important variance-covariance matrix, as a consistent accuracy measure of the parameter estimate, is provided by the developed error analysis. Further, the developed theory can be easily generalized to other cases with more general assumptions about the measurement errors. [ABSTRACT FROM AUTHOR]

• This article analyzes the distribution characteristics of different terrains. To ensure the accuracy of the training model, we use shuttle radar topography missions (SRTMs) with different terrains to obtain simulated learning training data. • This paper uses a refined d -LinkNet to accurately estimate the phase gradient ambiguity numbers and is combined with the small window median filter to obtain the vertical and horizontal phase gradients. • The proposed method is the first time to combine deep-learning and UKF for 2-D PU. The proposed method has better noise robustness than the existing PU methods and can obtain better unwrapped results than other methods in areas with large-gradient changes. Two-dimensional phase unwrapping (2-D PU) has a strong influence on the accuracy of interferometric synthetic aperture radar (InSAR) data processing results. Phase gradient estimation (PGE) is one of the key steps in the processing of 2-D PU. Moreover, the accuracy of the PGE will directly affect the accuracy of the final PU result. The phase continuity assumption is an important prerequisite for the PGE of traditional 2-D PU methods. However, the accuracy of PGE is not ideal in areas with high-noise and large-gradient changes. To address this issue, in this article, we propose a 2-D PU method of an unscented Kalman filter (UKF) using a refined LinkNet with a pretrained encoder and dilated convolution (D-LinkNet). To the best of our knowledge, this article is the first time to combine deep-learning and UKF for 2-D PU. First of all, this article analyzes the distribution characteristics of different terrains. To ensure the accuracy of the training model, we use shuttle radar topography missions (SRTMs) with different terrains to obtain simulated learning training data. Then, the refined d -LinkNet method is used to accurately estimate the gradient ambiguity numbers and is combined with the small window median filter to obtain the vertical and horizontal gradients. Finally, the UKF model is used for 2-D PU. Experiments are conducted with simulated and TanDEM-X InSAR datasets. In addition, compared with the existing PGE methods and 2-D PU methods, the experimental results show that the proposed method can obtain more accurate results than the existing methods. [ABSTRACT FROM AUTHOR]