Weighted mean temperature (T_m) is a critical parameter in Global Navigation Satellite System (GNSS) technology to retrieve precipitable water vapor (PWV). It is convenient to obtain high-precision T_m estimates near surface utilizing Bevis formula and surface temperature. However, some researches pointed out that the Bevis formula has large uncertainties in high-altitude regions. We investigate the applicability of the Bevis formula at different height levels and find that the Bevis formula has relatively high precision when the altitude is low, while with altitude increasing, the precision decreases gradually. To solve the problem, we analyze the relationship between T_m and atmospheric temperature within the near-earth space range (the height range between 0~10km) and find that they have a high correlation on a global scale. Accordingly, we build a global weighted mean temperature model based on near-earth atmospheric temperature. Validation results of the model show that this model can provide high-precision T_m estimation at any height level in the near-earth space range.

Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation systems of a global navigation satellite system (GNSS) and inertial navigation system (INS),the conventional RAIM algorithm has been developed to extended receiver autonomous integrity monitoring (ERAIM). However, the ERAIM algorithm may fail and a false alarm may generate once the measurements are contaminated by significant outliers, and this problem is rarely discussed in the existing literatures. In this paper, a robust fault detection and the corresponding data processing algorithm are proposed based on the ERAIM algorithm and the robust estimation. In the proposed algorithm, weights of the measurements are adjusted with the equivalent weight function, and the efficiency of the outlier detection and identification is improved, therefore, the estimates become more reliable, and the probability of the false alarm is decreased. Experiments with the data collected under actual environments are implemented, and results indicate that the proposed algorithm is more efficient than the conventional ERAIM algorithm for multiple outliers and a better filtering performance is achieved.

For the determination of the smoothing factor (also known as the regularization parameter) in the co-seismic slip distribution inversion, the compromise curve between the model roughness and the data fitting residual is generally used to determine (in order to distinguish the method proposed in this paper, the method is called “L curve” according to its shape). Based on the L-curve, the Eclectic Intersection curve as a new method is proposed to determine the smoothing factor in this paper. The results of the simulated experiment show that the inversion accuracy of the parameters of the seismic slip distribution with the smoothing factor determined by the Eclectic Intersection curve method is better than that of the L curve method. Moreover, the Eclectic Intersection curve method and the L curve method are used to determine the smoothing factor of L’Aquila earthquake and the Taiwan Meinong earthquake slip distribution inversion respectively, and the inversion results are compared and analyzed. The analysis results show that the L’Aquila and the Taiwan Meinong actual earthquake slip distribution results are in the range of other scholars at home and abroad, and compared with the L curve method, the Eclectic Intersection curve method has advantages of high computation efficiency, no need to depend on data fitting degree and more appropriate of smoothing factor and so on.

Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring (MHSS ARAIM) algorithm, such as the weaker robustness, a number of computational subsets with the larger computational load, a method combining MHSS ARAIM with gross error detection is proposed in this paper. The gross error detection method is used to identify and eliminate the gross data in the original data first, then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection. Therefore, this makes up for the weakness of the MHSS ARAIM algorithm. With the data processing and analysis from several international GNSS service (IGS) and international GNSS monitoring and assessment system (iGMAS) stations, the results show that this new algorithm is superior to MHSS ARAIM in the localizer performance with vertical guidance down to 200 feet service (LPV-200) when using GPS and BDS measure data. Under the assumption of a single-faulty satellite, the effective monitoring threshold (EMT) is improved about 22.47% and 9.63%, and the vertical protection level (VPL) is improved about 32.28% and 12.98% for GPS and BDS observations, respectively. Moreover, under the assumption of double-faulty satellites, the EMT is improved about 80.85% and 29.88%, and the VPL is improved about 49.66% and 18.24% for GPS and BDS observations, respectively.

A systematic and comprehensive comparison of the five commonly used earth radii in geodesy and cartography is carried out, and the differences between the most common points of the earth radii, their corresponding maximum values, and the latitudes of equal points between them are derived with the help of computer algebraic systems. The symbolic expressions are expressed as a power series of the first eccentricity. Taking the CGCS2000 ellipsoid as an example, the differences between the commonly used earth radii are clarified to numerical values. The results show that the difference between the commonly used earth radii has a maximum at 90 degrees and a minimum at 0 degree. The difference between the average radius of curvature and the rectifying sphere radius is the biggest, and the difference between the average radius of curvature and the average sphere radius is the smallest. These results can provide a theoretical basis for corresponding research in the geosciences, space science, navigation and positioning.

It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree (MST) tessellation considering shape information and the RHMRF-FCM algorithm. It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist. By using the MST model and shape information, the object boundary and geometrical noise can be expressed and reduced respectively. Firstly, the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented. Secondly, based on the tessellation results, the RHMRF model is built, and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function. Finally, the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results. To verify the robustness and effectiveness of the proposed algorithm, the experiments are carried out with WorldView-3 (WV-3) high resolution image. The results from proposed method with different parameters and comparing methods (multi-resolution method and watershed segmentation method in eCognition software) are analyzed qualitatively and quantitatively.

The paper presents a geometric calibration method based on the sparse ground control points (GCPs), aiming to the linear push-broom optical satellite. This method can achieve the optimal estimate of internal and external parameters with two overlapped image pair along the charge-coupled device (CCD), and sparse GCPs in the image region, further get rid of the dependence on the expensive calibration site data. With the calibrated parameters, the line of sight (LOS) of all CCD detectors can be recovered. This paper firstly establishes the rigorous imaging model of linear push-broom optical satellite based on its imaging mechanism. And then the calibration model is constructed by improving the internal sensor model with a viewing-angle model after an analysis on systematic errors existing in the imaging model is performed. A step-wise solution is applied aiming to the optimal estimate of external and internal parameters. At last, we conduct a set of experiments on the ZY-3 NAD camera and verify the accuracy and effectiveness of the presented method by comparison.

Mapping function errors are usually not taken into consideration, when space geodetic data observed by VLBI, GNSS and some other techniques are utilized to estimate troposphere delay, which could, however, probably bring non-ignorable errors to solutions. After analyzing the variation of mapping function errors with elevation angles based on several-year meteorological data, this paper constructed a model of this error and then proposed a two-step estimation method of troposphere delay with consideration of mapping function errors. The experimental results indicate that the method put forward by this paper could reduce the slant path delay residuals efficiently and improve the estimation accuracy of wet tropospheric delay to some extent.

This paper focus on solving the problem of seafloor control point absolute positioning with low vertical accuracy based on the survey ship sailing circle. The method of dealing with the systematic error based on a semi-parametric adjustment model was proposed. Firstly, the influence of sound velocity change on ranging error is analyzed. Secondly, a semi-parametric adjustment model for determining three-dimensional coordinates of seafloor control points was established. And respectively proposed solutions under two different conditions, the observation duration is an integral multiple or non-integer multiple of the long-period term of the ranging error. The simulation experiment shows that this method can obviously improve the accuracy of vertical solution of seafloor control point compared with the difference technique and the least-squares method when internal waves exist and observation duration is less than an integer multiple of the long-period term of the ranging error.

With regard to the inferior techniques and low accuracy of phase center calibration of an antenna array, this paper proposes a new calibration method considering the actual antenna pointing by introducing a precise engineering surveying technique to measure the real state of antennas. First, an industrial photogrammetric system is utilized to obtain the coordinates of points on antenna panels in different postures, and the actual pointing of the mechanical axis is obtained via least-squares fitting. Then, based on this, the coordinates of antenna rotation center are obtained by seeking the intersection of mechanical axes via using the matrix method. Finally, the mechanical axis in arbitrary postures is estimated based on the inverse-angle weighting interpolation method, and the reliable phase center is obtained by moving a fixed length from the projective center along the mechanical axis. An uplink antenna array including three ?3m antennas is taken as experimental object, and all photogrammetric coordinate systems are unified by the engineering control network, with each antenna phase center precisely calibrated via the proposed method. The results of electrical signal synthesis indicate that this method can effectively overcome the influence of gravity deformation and mechanical installation error, and enhance the synthetic signal magnitude of the uplink antenna array.

Independent of traditional approach of satellite altimeter calibration, the feasibility of altimeter validation using tide gauge located on solitary island at open sea (TGSI) and deep-ocean bottom pressure recorder (DBPR) separately is initially studied. Bias of Jason-3 sea surface height (SSH) and relative SSH bias (Δbias) between Jason-2 and Jason-3 is calculated using the data of tide gauge on Harvest oil platform, tide gauge No. 1890000 and DBPR No. 21419. The standard deviations of calculated SSH bias sequence are 3.98cm, 2.87cm and 8.61cm respectively, and Δbias (Jason-3—Jason-2) is -3.62±2.17cm, -2.58±1.97cm and -2.60±1.30cm respectively. Comparing to the results reported by international calibration sites, the results show that Jason-3 SSH is 3.0cm lower than that of Jason-2, the selected DBPR is appropriate to the calculation of relative SSH bias between Jason-2 and Jason-3, but it is not suitable for calibration or validation of single satellite, TGSI is appropriate to both.