2016 Vol. 41, No. 12
Meteorological fields represent a multidimensional dynamic environment; therefore visualization is a means study inherent regularities in these phenomena. In this paper, aiming to visualize the worldwide, multi-dimensional, multi-scale, massive characteristics of meteorological field data, a voxel object-oriented data model and a voxel based multi-level indexing mechanism were designed. A visualization method for 3D meteorological fields is proposed, including an arrow model based visualization method for vector fields and improved 3D texture-mapping based volume rendering method for scalar fields. Visualization and spatio-temporal retrieval experiments on a Virtual Globe platform were carried out to demonstrate the feasibility and effectiveness of the proposed methods.
In the uniform iterative algorithm for hydrodynamic flood routing model, the iteration step and the actual required time of each grid are inconsistent, and directly affects the precision of water level and discharge estimation for each grid cell. To solve this problem, a double-layer asynchronous iterative algorithm is proposed. The outer iterative process controls the flood routing time, while the inner iterative process adaptively selects the step size and the times for iterations by analyzing the flood velocity characteristics of different grid cells. FloodArea, the uniform iterative algorithm and the double-layer asynchronous iterative algorithm are applied to simulate historical heavy rain flood processes in the Wanan basin in Fujian province. Experimental results show that the simulated results of the double-layer asynchronous iterative algorithm are most consistent with actual disaster data. The average error in the proposed algorithm is less than FloodArea, 0.361 m and less than the uniform iterative algorithm, 0.654 m. The proposed method effectively improves the integral accuracy of flood routing simulation.
In this article, a method for constructing an approximate equal area grid based on the octahedron and Snyder projection is introduced. An octahedron that has equal surface area to a sphere is built; each face of this octahedron is considered as initial subdivision surface. Then, each initial surface is subdivided into hierarchical triangles using a quaternary triangular subdivision scheme, which are projected onto the surface of sphere using Snyder projection. The arc projecting polyhedron triangles onto the spherical surface is modified by using great circle line instead of Snyder projection arc. A new approximate equal area global discrete grid system is constructed. Based on the analysis of difference between the great circle arc and Snyder projection arc, the values of area, edge length, and angle of different subdivision level grids are calculated according to corresponding spherical calculation equation. Based the calculation results, different levels of approximately equidistant grid areas, lengths, angles, and spatial distributions of the deformation are analyzed. Results indicate that with increasing osubdivision levels, 1) the difference in grid areas is very little; area errors of 99.8% of the grids are between -10% and 10%. The girds with heavy area distortion are near the lines between the middle point and three vertexes of octahedron surface, when subdivision level is equal to 10; 2) the ratio increments of the maximum and minimum values of grid areas and edge length show a trend toward convergence, converging to 1.73 and 3.03 respectively.
In recent years, spatial outlier detection has become a research hotspot in the domain of spatial data mining. The aim of spatial outlier detection is to discover those small parts of spatial entities deviating from the global or local distribution in massive spatial datasets. Spatial outliers may indicate potential, unknown, and important knowledge instead of noise in many application domains, e.g., environmental science, meteorology, urban traffic, and so on. Existing spatial outlier detection methods focus on detecting spatial outliers in the spatial datasets with non-spatial attributes. There is still a lack of detection methods specifically designed for spatial point event datasets, in particular, for complicated spatial point event datasets with clusters having arbitrary shapes and/or different densities. Therefore, we developed a method of detecting outlier patterns for spatial point events by considering spatial locations; the definition of a spatial outlier is extended and a multi-level constrained Delaunay triangulation is employed. Spatial adjacency relationships are roughly obtained from Delaunay triangulation. Then, three-level constraints are described and utilized for precise spatial adjacency relationships with the consideration of statistical characteristics. Finally, those spatial point events connected by the remained edges are gathered to form a series of clusters. The clusters containing very few point events are regarded as spatial outlier patterns. This algorithm does not involve any parameters. Experiments on both synthetic and real-world spatial datasets demonstrate that this algorithm can detect all kinds of spatial outlier patterns efficiently and robustly.
Similarities exist in spatial patterns over time, such as in serial landscapes of an area at different times, serial expressions of an area in difference scale maps, spatial patterns in different areas and so on. These similarities often refer to spatial attribute (i.e., location, shape, size) and non-spatial attribute data (i.e., type, quality). This paper presents a "flowing water model" to evaluate similarities between different spatial patterns. Both global and local situation of landscape features are considered in this method. This method uses flow overlay areas for time points to describe the landscape patterns instead of landscape indexes. To some extent it avoids the uncertainty that one value may describe more than one type of landscape indexe and disturbances in spatial correlation. We explored the similarities of Beijing traffic-network backbones from 1938 to 2014, revealing the tendency of the traffic-network pattern and change in this pattern over time.
Due to the limitations of the accuracies in dense image matching and the factors of simplification during the three dimensional mesh generation, enormous noises normally occur on the triangular mesh surfaces during object reconstruction procedures. Furthermore, the geometrical and radiant differences between images, together with the noises, cause the textured mesh fragmentation. Aiming to solve this problem, this paper proposes a texture mapping method using a local surface consistency constraint. In this mapping procedure, fragmented triangles are merged into a relative larger surface with region growing, considering continuity and planarity. The regions are treated as a rigid surface and mapped to the same image to relieve the fragmented effect in the textured mesh. Experimental results show that the proposed method is effective and the output has better consistency than industry standard software such as street factory.
A new accuracy assessment framework for interpolations, based on a variance-scale law, is proposed in this paper. Based findings in different research areas, it has been shown that the variance of a certain variable decreases with agrowing scale. According to this theoretical law, three presupposed assessment criteria have been proposed. Application cases in DEM interpolation were selected and four common methods, i.e., HASM, Spline, Kriging, and IDW were chosen for comparison in this new framework. The second assessment criterion, regarding changing trends, was found to be questionable in a numerical test. The results of two real-world DEM interpolation cases indicate that the theoretical changing trend in the variance-scale relation cannot be obtained in real terrain cases because of an complicated integrated effects, characteristic of the real-world terrain, and the smoothing effect of interpolators and interpolation scales. However, as the original sampling variance was introduced into the framework, the general changing trend of variances and the overall level of variances at most scales could still be used as accuracy assessment measures even under different sampling densities. After modifying the second and the third criterion, the final three criteria in this assessment framework were established. Both the numerical surface case and real-world DEM examples indicate that this assessment framework is simple in theory, convenient for usie, and is an objective and effective assessment method, widening the field of accuracy assessment in DEM interpolation. Moreover, based on the results of different sampling densities, this framework can also provide valuable application suggestions for choosing suitable interpolation methods in real-world cases.
In this paper, we present an automatic registration method based on planar primitive groups for building point clouds. This method distinguishes planar features with similar structures found in urban scenes, and reduces feature matching search complexity. In the method, planar patches with similar normal vectors are defined as a planar primitive group. We extract planes from point clouds as planar primitives. Using a threshold, we cluster the planar primitives with the similar normal vectors into groups. Finally, we match the planar primitives in groups, and calculate transformation parameters with an extended quaternion method. Experimental results show that this method is effective for automatic registration of building point clouds.
In this paper, a new fast, stable automatic detection method for round ban traffic signs is proposed. Color segmentation based on RGB color space is conducted on sign images. Given the poor adaptability of segmenting by calculating direct difference between channels and setting fixed thresholds, a novel self-adaptive method is proposed that calculates the relative channel difference and fitting threshold curve based on selecting a basic channel. Meanwhile, a gradient filter method separates the signs from its background with the same red color. Edges are extracted and a method of error estimation after least square ellipse fitting screens out the sign edges. Experimental results show that the method presented in this paper can automatically detect round ban traffic signs from sign images of deferent brightness in natural environments, with good prospects in Intelligent transportation as it is stable and applicable in real-time.
Based on the observation data of ground PM2.5/PM10, GPS Precipitable Water Vapor (PWV) and Radiosonde PWV in Beijing in 2013, the change among PM2.5/PM10, GPS PWV and Radiosonde PWV per layer of PWV was compared. We found that there is significantly positive correlation between ground PM2.5/PM10 variation and ground GPS PWV change during autumn, winter, and spring. Such a correlation does not exist in the rainy summer months. According to the comparisons between ground PM2.5/PM10 observations and aerial PWV Radiosonde observations made in autumn, winter, and spring, the correlation between ground PM2.5/PM10 variation and total aerial PWV change is also significantly positively related. The correlation between PM2.5/PM10 variation and the 3th/4th water vapor layer (850-3 000 m) PWV change is the most significantly positively related value.
An improved TurboEdit Algorithm for BDS is analyzed and validated based on the characteristics of BDS with three types of satellites. A fixed window sliding method is used instead of the original recursive method for detecting cycle slips with M-W combination observations. The original ionosphere residual combination detection method is improved by including the adjacent epoch differential method for double-frequency ionosphere residuals, the detection parameters can be selected for GEO/IGSO/MEO satellites. During actual data processing, the improved algorithm is more stable and yields more accurate detection results than the original algorithm, especially in detection of small cycle slips. Also, the improved algorithm can effectively compensate for the original algorithm that does not easily detect small cycle-slips. Experimental results show that the improved algorithm can effectively detect all kinds of cycle slips such as equal-cycle, small cycle slips and big cycle slips. The repair precision can reach the 0.1 cycle.
In this paper, we got the optimal linear combinations which can effectively detect and repair all types of cycle slips considering the characteristics of BeiDou Navigation Satellite System(BDS). In the detection process, five geometry-free linear combinations were applied based on a three-step cycle slip detection process. For different sorts of cycle slips, specific frequency carrier phase linear combinations are used for repairs. Finally, based on Beidou triple-frequency data with different levels of ionospheric delay variation under different sampling intervals of 1 s, 15 s, 30 s respectively, the ability of cycle slip detection and repairs were verified. The results were optimum.
In this paper, a novel Frequency Hopping(FH) communication scheme for communication between a satellite and ground station based on the Beidou time service and satellite ephemeris is proposed. Satellite orbits are predicted by the SGP4 model based on satellite ephemeris and the propagation delay and Doppler shift are estimated. The estimated results are used for pre-correction and compensation at the ground station. Results show that t propagation delay estimation accuracy is better than 300 ns, so the FH rate reaches 20 000 Hops/s with a cost of 1%. The normalized Doppler shift error is less than 1×10-8. The proposed method can operate under low SNR conditions or strong jamming, as it does not rely on the pilot or sync head. This optimized scheme has lower complexity and better anti-jamming capability.
Differential Code Biases (DCBs) are the main systematic error in ionosphere TEC monitoring and modeling. Meanwhile, satellite DCBs are important parameters for satellite navigation messaging. This article presents a satellite DCBs estimation algorithm and DCBs transformation formula derived under different zero-mean conditions as applied to the constellation average of the satellite DCBs. Using BeiDou Experimental Tracking Station (BETS) observations from 2013, the DCBs for BDS satellite were determined, and the BDS satellite DCBs monthly stability was analyzed and compared with the DCBs products published by MGEX under the same zero-mean conditions. Results show that the BDS satellite B1-B2 DCBs values are between-9~17 ns, and the stability was better than 0.4 ns. Stability for BDS IGSO satellites was better than GEO and MEO satellites. The BDS satellite DCBs determined using BETS and MGEX have system biases, where the largest discrepancy was about 1.7 ns. The probable reason lies in a discrepancy in the pseudo range code measurement. The difference of receiver material results in a discrepancy in receiver DCBs.
As one of the main error sources in the Global Navigation Satellite System (GNSS), the accuracy of tropospheric delay correction model influences the estimated coordinates, especially in vertical direction; therefore, it is necessary to make assessment of different tropospheric delay correction models. An assessment of four commonly used models, including EGNOS/UNB3m/GPT/GPT2, is made in this paper. The result shows that the RMS of the four models remains 4-5 cm, and the difference between each model is less than 1 cm, with GPT2 being the most accurate model; and the RMS of each model in summer is relatively much bigger than in winter, due to the abundant water vapor in summer, which makes it harder to model tropospheric delay precisely. All models show poor accuracy in southeast China, thanks to the rather changeable weather and the abundant water vapor there; and no model is sensitive to the height, since the accuracy varies little with respect to the variation of altitude. These findings provide a reference for the GNSS users in China area when choosing a suitable tropospheric delay correction model.
Serial observed gravity anomaly data and a gravity anomaly referenced map for navigation can be used to correct the drifting errors of inertial navigation system based on the EKF. To address the problem of unknown gravity anomaly measurement noise due to an unpredictable gravimetric environment and disturbances to the measuring instruments, et al, a matching algorithm for gravity anomaly filtering based on residual errors can be used to estimate measurement noise variance adaptively; Residual-based Adaptive Estimation (RAE). A set of parallel Kalman filters were designed and a rule for selecting the best filter was simplified. RAE filtering experimental results show that the longitude and latitude drifting errors in inertial navigation systems can be reduced effectively based on the RAE filtering and positioning accuracy of the navigation system thus improved.
We propose a combined phase unwrapping algorithm using region partitioning. The wrapped phase image is partitioned into high and low quality areas according to its quality map, and a quality guided algorithm is performed to get the initial unwrapped result. A quality guided strategy is adopted to identify low quality areas, and each low quality area is stored separately. The low quality areas are optimized by a minimum discontinuity phase unwrapping algorithm one by one, and then the final unwrapped result is obtained. An unwrapping test performed on an InSAR interferogram shows that the proposed algorithm is more efficient than the minimum discontinuity phase unwrapping algorithm, and the unwrapped result was more accurate than results obatained from a quality guided algorithm.
Quality and reliability of geodetic data depend on the size of its uncertainty. In this paper, uncertainty in measurement data is effectivey evaluated based on the theory of measurement uncertainty and fuzzy mathematics, A Function model using measurement uncertainty as unknown parameter is established to directly evaluate the uncertainty of survey data. The Fuzzy Entropy Measure is proposed as the optimal criterion to solve the function model. A corresponding algorithm is established; Experiemental results and comparisons with the least squares estimation show that the proposed method using a elevation monitoring network data solver is feasible.
The Total Least Squares (TLS) method is applied to joint adjustment. An algorithm for total least squares joint adjustment with a weight scaling factor is derived. The weight scaling factor is the key to deal with joint adjustment, and methods for determining the weight scaling factor are discussed. The difference norm between the estimated and true values is used to evaluate the TLS joint adjustment simulation. The influence of different noises on the weight scaling factor is also analyzed for two simulated examples. The results show that the estimated values is related to the weight scaling factor. When priori information is accurate, the prior unit weight variance method performs the best, and when priori information is inaccurate, the minimum discriminate function method
An urban ground noise field is made up of natural ground noise and cultural ground noise with different temporal and spatial distribution characteristics. In general, the natural ground noise satisfies three features of consistency, stability, and correlation with cultural ground noise appearing as non conformance, instability, and non correlation. Referring to their different characteristics, this paper proposed three principles on the urban ground noise separation and presents a improved method of multivariate separation to effectively separate the two types of noisy fields. We tested four types of standard signals and the results demonstrated superiority of multivariate separation. In addition, exploratory experiments in Shenzhen indicate that the proposed method of multivariate separation can divide urban ground noise signal effectively into two parts, and can be refined underground geological information. By separating the underground noise, we can quantitatively compare the noise level of natural ground noise and cultural ground noise.
In order to maintain and improve the smoothness of railroad tracks, they must be adjusted frequently. Profile fitting is a key step in the adjustment of existing railway lines; the fitting linear of profile determines if track profile adjustment is reasonable and necessary. Circular curve fitting is essential to profile fitting of the existing railway lines. The circular curves on railway profile have short arcs and long radii, which results in difficultlies when fitting accurate curve parameters, in conformity with railway profile linear characteristics. In this paper, we provide a new fitting method applicable to circular curves in railway profiles, according to two characteristics. This method has fewer fitting parameters, and higher fitting accuracy characteristics. In addition, the resulting circular curve parameters meet the profile rail linear requirements, which can provide a reference for existing railway line profile circular curve fitting.