Time correlations always exist in modern geodetic data, and ignoring these time correlations will affect the precision and reliability of solutions. In this paper, several methods for processing kinematic time-correlated observations are studied. Firstly, the method for processing the time-correlated observations is expanded and unified. Based on the theory of maximum a posteriori estimation, the third idea is proposed after the decorrelation transformation and differential transformation. Two types of situations with and without common parameters are both investigated by using the decorrelation transformation, differential transformation and maximum a posteriori estimation solutions. Besides, the characteristics and equivalence of above three methods are studied. Secondly, in order to balance the computational efficiency in real applications and meantime effectively capture the time correlations, the corresponding reduced forms based on the autocorrelation function are deduced. Finally, with GPS real data, the correctness and practicability of derived formulae are evaluated.

The current underwater terrain surface matching algorithm, which uses Hu moment as the similarity index, cannot gain accurate location due to the algorithm's disadvantage in detecting slight differences. To solve this problem, a line-surface integrated terrain matching algorithm is presented. First, the similarity evaluation method of the traditional Terrain Contour Matching (TERCOM) algorithm is improved, and the strategy used to select the matching regions is developed. Then, a surface matching algorithm based on the geometric similarity is established to determine the optimum match between the reference maps and the actual measured terrains. Finally, a means of “line matching algorithm” and “surface matching algorithm” integration is proposed based on a fixed threshold. The experimental results show that the proposed algorithm can obtain a more accurate location and has greater robustness than that of the surface underwater matching algorithm based on Hu moment.

The determination of the calibration parameters of the gravity gradiometer play an important role in the GOCE gravity gradient data processing. In this paper, the temporal signals and outliers in the GOCE gravity gradient observations are analyzed. Based on the different global gravity field models, the scale factors and biases are determined in all the components of GOCE gravity gradients. And then the accuracy of the calibration results is validated. The results indicated that the effect of the ocean tide is at mE magnitude in the measurement band, which is equivalent to the precision of the gravity gradiometer, while the effect of the non-tide temporal signals, such as terrestrial water is in the order of 10 ^-4E, is slightly less than that of the ocean tide. The outliers in all the gravity gradient components are larger than 0.2%. And after the calibration using global gravity field models except EGM96, the stability of scale factors in the V_xx、V_yy、V_zz、V_yz components reaches 10 ^-4 magnitude, and the V_xz component reaches 10 ^-5 while that of the V_xy component is about 10 ^-2, which are in accordance with the accuracy differences of the gradient components.

Volume parameter is the basic content of a spatial body object morphology analysis. However, the challenge lies in the volume calculation of irregular objects. The point cloud slicing method proposed in this study effectively works in calculating the volume of the point cloud of the spatial object obtained through three-dimensional laser scanning (3DLS). In this method, a uniformly spaced sequent slicing process is first conducted in a specific direction on the point cloud of the spatial object obtained through 3DLS. A series of discrete point cloud slices corresponding to the point cloud bodies are then obtained. Subsequently, the outline boundary polygon of the point cloud slicing is searched one by one in accordance with the slicing sequence and areas of the polygon. The point cloud slice is also calculated. Finally, the individual point cloud section volume is calculated through the slicing areas and the adjacent slicing gap. Thus, the total volume of the scanned spatial object can be calculated by summing up the individual volumes. According to the results and analysis of the calculated examples, the slice-based volume-calculating method for the point cloud of irregular objects obtained through 3DLS is correct, concise in process, reliable in results, efficient in calculation methods, and controllable on accuracy. This method comes as a good solution to the volume calculation of irregular objects.

Low earth orbit satellites, with unique advantages, are prosperous types of navigation augmentation satellites for the GNSS satellites constellations. The broadcast ephemeris element needs to be designed as an important index of the augmented LEOs. The GPS ephemerides of 16/18 elements cannot be directly applied to the LEOs because of the poor fitting accuracies in along-track positional component. Besides, the ill-conditioned problem of the normal-matrix exists in fitting algorithm due to the small eccentricity of the LEO orbits. Based on the nonsingular orbital elements, 5 sets of ephemerides with element numbers from 16 to 19 were designed respectively by adding or modifying orbital elements magnifying the along-track and radial positional components. The fitting experiments based on the LEO of 300 to 1500km altitudes show that the fitting UREs of the proposed 16/17/18/18 ^*/19-element ephemerides are better than 10/6/4/5/2.5cm, respectively. According to the dynamical range of the fitting elements, the interfaces were designed for the 5 sets of ephemerides. The effects of data truncation on fitting UREs are at millimeter level. The total bits are 329/343/376/379/396, respectively. 29/15 bits are saved for the 16/17-element ephemerides compared with the GPS16 ephemeris, while 64/61/41 bits can be saved for the 18/18 ^*/19-element ephemerides compared with the GPS18 elements ephemeris.

Sound velocity profile (SVP) data is indispensable in the multi-beam data processing. The sampling density is of great importance for SVP to represent the vertical variation of sound velocity accurately and guarantee the accuracy of sound ray tracing (SRT). However, the SVP also affects the SRT efficiency significantly, especially in deep-sea multi-beam sounding data processing. To improve SRT efficiency and ensure SRT accuracy, an adaptive SVP simplification method based on area difference is proposed in this article. Firstly, the relationship between the area difference of the raw SVP and the simplified one and SRT bias is studied, and the relationship model of them is built. Then, by considering the constraint of SRT accuracy, the SVP simplification method and the simplifying SVP procedure SVP are given. Finally, a deep water experiment is conducted to verify the proposed method. Compared to the existing method, the proposed method improves the robustness, feasibility of SVP simplification as well as the accuracy and efficiency of SRT.

An object-based approach is proposed for land cover classification using optimal polarimetric parameters. The ability to identify targets is effectively enhanced by the integration of SAR and optical images. The innovation of the presented method can be summarized in the following two main points: ①estimating polarimetric parameters (H-A-Alpha decomposition) through the optical image as a driver; ②a multi-resolution segmentation based on the optical image only is deployed to refine classification results. The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area, California. The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database (NLCD). A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%, with an overall accuracy of 92.6% over regions with rich texture.

In this paper, we propose a new method to achieve automatic matching of multi-scale roads under the constraints of smaller scale data. The matching process is: Firstly, meshes are extracted from two different scales road data. Secondly, several basic meshes in the larger scale road network will be merged into a composite one which is matched with one mesh in the smaller scale road network, to complete the N∶1(N>1) and 1∶1 matching. Thirdly, meshes of the two different scale road data with M∶N(M>1,N>1) matching relationships will be matched. Finally, roads will be classified into two categories under the constraints of meshes: mesh boundary roads and mesh internal roads, and then matchings between the two scales meshes will be carried out within their own categories according to the matching relationships. The results show that roads of different scales will be more precisely matched using the proposed method.