2018 Vol. 43, No. 10
GNSS receiver could not be used in indoor environments. This paper proposes a novel pseudolite positioning approach utilizing carrier phase difference measurements, which can achieve sub-meter accuracy without the support of a base station, and without the need of synchronization between pseudolites. In addition, in the proposed approach, the integer ambiguity resolution is unnecessary as well. This research builds up system model for pseudolite positioning and proposes basic principle for this positioning method utilizing carrier phase difference measurements. First, two sets of carrier phase measurements output from a dual-antenna receiver are differentiated in order to cancel out the common errors. Then a non-linear least square adjustment method is used to solve spatial coordinates of center point of the dual-antenna iteratively. Simulation results and experiment outputs from a dual-antenna software receiver verified the feasibility of this approach. This pseudolite positioning approach can be regarded as an effective complement of existing indoor positioning techniques.
This paper firstly compares and analyzes the effect of maximum like lihood estimation (MLE) and least square variance estimation (LS-VCE) and minimum norm quadratic unbiased estimation (MINQUE) in the variance component estimation of GPS coordinate time series noise. After determining the minimum norm of two unbiased estimation method for the optimal estimation of noise variance, the correlations between the noises in each direction are analyzed by means of unitary linear regression, and the linear regression equations are determined.Experimental results show that, compared with the least squares variance component estimation method and maximum likelihood estimation method, MINQUE has a better estimation effect for the noise variances of GPS coordinate time series.In addition, the influence of noise on the estimation of motion parameters of the station can be reduced by using long span time series.Moreover, the same kind of noise in GPS coordinate time series of each direction has more significant correlation and the correlation between flicker noise in north and other directional flicker noises are better than that of white noises. The 40%-60% of the variance of the vertical noise can be explained by the noise variance in the horizontal direction, and north noise variance can explain the 60%-80% of change of the noise variance in east.The obtained linear regression equation has practical value.
Antarctic subglacial lakes form an important component of the subglacial hydrological system. The movements of some subglacial lakes have great impacts on subglacial hydrology, ice-flow velocity, mass balance of ice sheet and sea level changes. According to the deformation of the ice-sheet above the subglacial lakes, the repeat-track algorithm based on ICESat altimetry data was introduced to detect the height changes of subglacial lakes. Then simulative models were established to analyze the ice-sheet deformation caused by the different patterns of active subglacial lakes. Finally, the Aca-demy12 and CookE2 subglacial lakes were selected to analyze the actual application of the algorithm. The results show that the ice-sheet above the Academy12 and CookE2 subglacial lakes went up 6 m and went down 70 m respectively. All the altimetry data applied in the repeat-track algorithm is capable of detecting the right height changes when the height change patterns are steady. However, when there exists high variability of the height change patterns, the proper altimetry data should be adopted to calculate the topography in order to get the right height changes.
This paper is focused on the problem that the increasing noise of state equations and observation equations lead to the divergence of Kalman filtering in the dynamic positioning navigation. A new method is put forward to calculate navigation fused weight. The new method is based on information entropy and adopts Gaussian mixture model as the framework. First, the main structure of underwater integrated navigation system and related state and observation equations of every sub-filters are provided. Then we studied the calculation method of the information entropy of each sub-filter, and the concept of entropy product is defined to calculate the weight of each component in the Gaussian mixture model. Finally, the computational process of the Gaussian mixture model filtering algorithm for entropy weighted underwater navigation information fusion is summarized. The simulation experiments show that the precision of the new method is much higher and the inhibiting ability against filtering divergence caused by noise is stronger than weighted Kalman filtering algorithm.
Global navigation satellite system reflectometry (GNSS-R) delay-doppler map (DDM) obtained with TechDemoSat-1 (TDS-1) is used to detect ice presence over Greenland region of Arctic. DDM of sea ice shows less spreading than those of open water as the scattered GNSS signals follow the coherenty scattering model and diffuse scattering model over ice and water respectively. The transition from seawater to sea ice could lead to decrease of the pixel number of DDM. A pixel number ratio of adjacent DDM based detection scheme is proposed through employing a threshold method to distinguish sea ice and sea water, and to analyze sea ice distribution of Greenland region. Sea ice distribution of Greenland with time is analyzed through using multi-period data from TDS-1. The performance of the proposed method is assessed with a mathematical statistical approach through comparing with ground-truth sea ice data from the National Snow and Ice Data Center, USA. The effectiveness of this approach is validated with a probability of detection up to 98.76%-99.21%, and the overall probability of detection is 99.09%.
Conventional gravity field measured on surface reflects the superposition of effects of topographic masses, local gravity anomalies owing shallow geological structures to a regional gravity field caused by deep tectonics. In this paper, we implement different depth layes' regional and residual anomaly separation based on robust 2D polynomial fitting with scalable moving window and weighting function determined by grid distances from surrounding points to central point. The algorithm overcomes the limitations of potential field separation merely in horizontal plane and vertical qualitative research by traditional algorithms. The validity of the algorithm proposed is verified by the data of synthetic models and field measured ground gravity data.
For precise orbit determination of deep space exploration, precision of traditional two-way range-rate model is limited by computers' word length, where the main error source is the simple differenciation of two subsequent Newtonian ranges(up-leg and down-leg) devided by the counting interval. So interplanetary two-way range-rate model is built up in this paper to compute these two necessary differences highy precisely. Of course, we give the formula of this new model and its detailed steps as well as necessary recursion formula of Chebyshev differential polynomial. The new model is implemented in Wuhan University deep-space orbit determination and gravity recovery system-WUDOGS and two scenarios of simulation are adopted to validate it from two aspects:precision of calculation values and initial orbit based on Mars express mission. Simulating results show that the new model can improve precision of calculation values by two orders of magnitude, and reduce extra numerical error during orbit determination, which can provide reference for orbit determination in long-distance deep space exploration of high precision in China.
In GNSS high-precision applications, transmit satellite clock stability is one of the core fac-tors which can directly affect the data processing performance. In this paper, the short-term stabilities of GNSS clocks are investigated with 20 Hz dual-frequency ground-based observations. In date processing procedure, the receiver clock bias are removed with single difference between two satellites, and the night-time dual-frequency observations were used to form the ionosphere-free measurements to avoid the ionosphere delays. Thus, the troposphere delays are corrected with empirical model and mapping function for residual delays. The satellite clock bias are identified by Lag1 autocorrelation function, and the short-term stability of different GNSS satellite atomic clocks are analyzed with Allan deviation. The results show that the single-difference clock stability of GPS, GLONASS and BeiDou are almost on the same magnitude, which is about 10-10 degree on 0.05 second interval and 10-11 degree on 1 second interval. Thus, the single-different strategy can be validated for BeiDou radio occultation data processing.
Sidereal filtering (SF) is applied to reducing the error of static station observation caused by multipath. However, SF based on observation domain has some problems when used for static precise point positioning (PPP). Hence, an adapted algorithm named sphere multipath stacking (SMPS) is proposed and applied to PPP. The process of SMPS algorithm and the statistical algorithm that choose the appropriate cell size are presented in this paper. The experiment result that utilizes the data from 10 IGS stations shows that SMPS algorithm can reduce the multipath error of GNSS carrier phase observation and the accuracy of 1-3 hours positioning result improves clearly. The positioning accuracy improvement ratios of E, N, U directions are 41.59%, 38.60%, and 36.96%, respectively.
Once an earthquake occurs, nearby GPS stations are able to capture the coseismic and post-seismic deformation which is critic for the geoscience research and maintenance of the dynamitic reference frame, thus to identify and estimate them is also an important part of the GPS time series analysis. The comprehensive inspection method to automatic detect and estimate coseismic and postseismic deformation is proposed and employed here, which models the GPS time series and comprehensively considers coseismic offsets and their coherence from different sources, and RMS improvement to decide if the station is affected by an earthquake. If it is, trail-and-error is used to search the best decay time and then all parameters including coseismic and postseismic deformations are estimated through linear least squares method. Earthquake examples account for the effectiveness of the proposed method.
UAV(unmanned aerial vehicle) is convenient and low cost, water reflection can cause high light in the image and has adverse effect on UAV data processing and the DOM(digital orthophoto map) quality. We propose a method for automatic water high light detection and removal in single UAV image. Firstly, we extracted initial candidate highlight regions using multi-scale threshold detection in a high light component proposed in this paper and used the Grabcut algorithm to optimize them. Next, a decision tree was built according to the characteristics of water high light to eliminate the error detection. Then, high light regions were refined by the high light points nearby. Finally, we modified Criminisi algorithm to remove high light regions. In actual UAV images, the experimental results show that our method can remove the water high light well and is superior to other methods proposed by Mallick, Shen and Yoon in terms of PNSR(peak signal to noise ratio) and SSIM(structural similarity index) parameters and visual evaluation, and improves the DOM quality as well.
A new algorithm is proposed to calibrate the base matrix between 3D geometric shapes for calculating correspondences using functional maps, in which shape correspondences can be represented as the calibration operation between the base matrices constructed by the shape eigenfunctions. First, the Laplace operators of 3D shapes are calculated to obtain eigenvectors and eigenvalues, and the basis matrix is constructed using the eigenvectors. Second, a calibration algorithm based on covariance mini-mum is proposed to calculate a calibration matrix S between shapes, and used to calibrate the basis matrices of function space of the two given shapes. Third, the Gauss curvature of all points of the source shape is calculated to sample some feature points, and traverses all points on the calibrated target model in order to find the optimal corresponding points to construct the correspondence between 3D shapes with isometric transformation (or approximate isometric transformation). Finally, the matching accuracy of the proposed algorithm is measured by calculating the geodesic error between the sampling points and the optimal points. Experiment results show that our algorithm is better than existing methods for establishing an accurate correspondence between two or more shapes, moreover, it significantly solves symmetry ambiguities problem which influence calculation of shape correspondence.
When the variance-covariance matrix of the error is fairly symmetric and positively definite, parameter estimation and accuracy assessment of weighted total least squares adjustment with inequality constraints(ICWTLS) are investigated in this paper. First, the problem of minimizing the residual sum of squares are converted to an optimization problem with only the model parameters under the total least squares, and all the inequality constraints are transformed into an equivalent aggregate constraint. Accordingly, the ICWTLS problem is converted to unconstrained optimization problem by a penalty function approach and is then solved by BFGS optimization method. Then, the observation equation and constraints function are expanded to the first order Taylor series and the linear approximation between the solution and observations is derived as well as the approximate dispersion matrix of the solution under the variance propagation law. Finally, a numerical simulation is given to indicate the validity and feasibility of this method.
DInSAR technique is easily influenced by decorrelation of time and space and atmosphere delay, SBAS(small baseline subset technique) was applied to process 13 scene of TerraSAR-X data. The residual DEM error, atmospheric delay error and orbit error are estimated and removed. The maximum subsidence rates of 2310 and 1301 working faces were 40 mm/a and 50 mm/a respectively by analyzing the subsidence rate of the coal mine area from 2012 to 2013. It found that the land subsidence of 2306, 2308, 2310 working faces is not obvious before November 15, 2012 by analyzing the timing cumulative subsidence map. Three candidate points of slowly decorrelation filter phase in the 2310 and the 1301 working faces were extracted respectively to be analyzed and we found that the relationship between sedimentation value and time was linear, moreover, the earlier the mining time was, the more the linear variation of the sedimentation characteristics was. Cumulative settlement values obtained by the SBAS and DInSAR were compared and analyzed and turned out that the difference between the two methods was less than 5 mm. The time sequence subsidences of several points in the trending and orientation of 2310 working face were selected and extracted, the surface subsidence of study area in different time period was quantitative analysis by analyzing the displacements of these points. Experiments show that SBAS-InSAR technology has a good application prospect in the monitoring and analysis of surface subsidence in mining area.
Discovery of regional spatial colocation patterns facilities understanding of the spatial dependency of different spatial features at the regional scale. However, two challenges remain:①appropriate thresholds for prevalence measures are difficult to specify without prior knowledge; and ②natural localities of regional spatial colocation patterns with different densities and shapes can hardly be automatically detected. On that account, an automatic method for discovering significant regional spatial colocation patterns is proposed in this paper. First, a nonparametric statistical model is developed to test for significance of spatial colocation patterns. Then, an adaptive spatial clustering method is modified to detect hot spots of each candidate regional spatial colocation pattern that is not identified as a statistically significant spatial colocation pattern at the global scale. At last, all hot spots are iteratively expanded until no larger statistically significant localities can be detected. Comparison between this automatic method and an existing method is carried out with both simulated and ecological datasets. Experiments show that the regional spatial colocation patterns can be effectively detected with less subjectivity and prior knowledge by this automatic method.
This paper presents two dual antenna GNSS-R(global navigation satellite system reflectometry) soil moisture retrieval models with soil roughness compensation-an analytic model and an artificial neural network (ANN) model. Then a simulator for GNSS-R soil moisture retrieval is built in consideration of GPS L1 C/A code modulation. After that the impact of soil roughness is elaborated. The simulation results show that the roughness compensation is necessary for the analytic model when the RMSH(root mean square high) is larger than 0.010 m. The roughness compensation works well for small roughness, but there are some limitations for large roughness. Under the situation where RMSH is greater than 0.025 m, the accuracy of ANN model is 36.83%-72.36% higher than the analytic model without roughness compensation, and the accuracy of ANN model is 42.86%-54.40% higher than the analytic model with roughness compensation. The ANN model achieves similar accuracy regardless of roughness compensation, and the accuracy of ANN model without compensation is still 35.83%-53.48% higher than the analytic model with compensation.
Based on the complex topographical features and sparse uneven observation sites in Sichuan Province, terrain factors (slope and aspect) and vegetation index were introduced in this paper. The method of (mixed) geographically weighted regression kriging ((m)GWRK) which took into account the non-stationary of spatial relationship was adopted to study the interpolation method of monthly mean temperature and the precision analysis of the estimation results. In different seasons and different regions, the estimation results of (m)GWRK and regression Kriging (RK) based on global regression were compared. The results indicate that the coefficient of determination(R2) of regression relationship of RK, GWRK and mGWRK are 0.795, 0.922 and 0.911, respectively, and root meansquare error of these three methods are 0.83℃, 0.64℃, 0.55℃, respectively. This implies (m)GWRK is better than RK in ability to interpret the target variable and estimation accuracy. Compared with RK, the improvements of (m)GWRK on estimating monthly average temperature have the characteristics of seasonal and regional differences. The improvement is more significant in winter half year than in summer half year. And in northwest and southwest Sichuan, where topography changes acutely, the improvement is greater than in basin where topography changes gently.
Leaf spectrum is very important to estimate vegetation biochemical parameters. However, the spectrum obtained from remote sensing is pixel and canopy spectrum, therefore, it is necessary to transform the spectrum from canopy level to leaf level when estimating leaf biochemical parameters by remote sensing data. The scaling conversion function during downscales from pixel spectra, canopy spectra to leaf spectra was derived according to principles of geometrical optics model in this paper. First, PROSPECT model was used to simulate leaf spectra. Then, with the other parameters unchanged, the canopy spectra was simulated under different leaf area index(LAI) and leaf spectra by 4-scale model, and the relationship between leaf reflectance and sunlit canopy reflectance was found. Finally, two lookuping tables were established based on LAI to achieve transformation from canopy spectra to leaf spectra. One is used to describe the relations between the probability of observed sunlit canopy and observed illuminating background. The other is for scattering factor calculation. The result indicates that leaf spectra can be well converted from canopy spectra using 4-scale model. The proposed method is very effective and useful.
Selecting the drought monitoring results of remotely sensed vegetation temperature condition index (VTCI) for winter wheat at the ten-day intervals from 2008 to 2016 in the Guanzhong Plain, the weights of drought impact on wheat yields at the 4 main growth stages were determined by applying the best weighting method. Linear regression analysis was employed to study the correlation between the weighted VTCIs and wheat yields of counties, and the yield prediction was carried out at 1-, 2-and 3-ten day intervals between 2008 and 2016 by using the monitored VTCIs and forecasted ones by the autoregressive integrated moving average models. The results show that the weights of drought impact on wheat yields at the turning green stage, the elongation stage, the heading-filling stage and the dough stage are 0.035, 0.489, 0.427 and 0.049 respectively based on the best combination weighting approach of the improved analytic hierarchy method and the entropy method. There is a significant correlation between the weighted VTCIs and the ground-measured yields published in the related statistical yearbooks, indicating the accuracy of the estimated yields is high. The forecasted yield accuracies are quite high and decreased with the increase of the forecasting intervals.
There exists the shortage of traditional land use regression (LUR) model in losing information of predictor variables when simulating the air pollutant concentration. An improved model which combined principal component regression (PCR) and stepwise multiple line regression (SMLR)-LUR (PCA+SMLR) was developed to simulate the spatial distribution of PM2.5 in large area. Firstly, the correlation analysis was conducted to screen out effective predictor variables. Secondly, principal component analysis (PCA) was employed to transform effective predictor variables to principle components. Finally, all principal components were used to conduct SMLR to simulate the spatial distribution of PM2.5. Meanwhile, the reliability of the improved model was tested in Beijing-Tianjin-Hebei urban agglomeration. Experimental results of three models (PCR, SMLR and PCA+SMLR) were compared and analyzed. The results indicated that the PCA+SMLR model has an adjusted R2 of 0.883 by improving the contribution of the predictor variables. Besides, it is better than the traditional mo-del for accuracy index and the mapping results. Therefore, it can be concluded that the PCA+SMLR is a promising PM2.5 modeling method and could be very use-ful for air pollution mapping.
Geo-collaboration is a typical feature of virtual geographic environments (VGEs), but there is not a systemic method to bridge the gap between participants and VGEs platform for design, management and implement group collaboration with VGEs. This paper presents a novel method based on a new constructed role model. The role model has six components which are role playing, role authority, role awareness, role thinking, role behaviors, and role representation. Based on the role model, geo-collaboration methodology is presented form multi-aspects including a three-tier architecture designed, geo-collaboration plan, conflict detection and solving. Based on above achievements, a prototype is build which is a case to simulate interaction between human behaviors and global change by integrating the role model and collaborative virtual geographic environment. The result shows that the role model based geo-collaboration for VGEs is reasonable, which is valuable for further research on collaboratively dealing with complex geo-phenomena and geo-processes with VGEs.
The networks composition and configuration of the different types of cells in street networks constitute specific network-scapes. Inspired by the similarity between cell in road network and patch in landscape ecology, this paper references landscape metrics in landscape pattern analysis, and proposes a new approach for street networks pattern analysis-network-scape metric analysis. The procedure of the approach is to build cells and assign the types based on types of enclosing roads, and then the metrics of network-scape were computed. An exploratory analysis was performed, in which a correlation analysis and factor analysis are combined. We explained the meanings of main factors and representative metrics in the field of street networks analysis. Through this approach, 24 metrics were computed for 34 Chinese urban street networks, and four main factors were found, which are labeled:spatial distribution and diversity, maximum size and elongation variation, average elongation, and average size and shape complexity. These factors can reflect charac-teristics of street networks, including clustering and dispersion, type disversity, and shape regularity.
We propose an integrated approach to discover both zones and movement trajectories among zones, which referred to as zone-based movement pattern (ZMP), from taxi trajectory data. This method discovers the zones by merging ZMPs, which keeps the directionality of movement, thematic attributes and distance relationship of zones by the adjacent constraints consists of distant and thematic attributes. By joint average frequencies, we can identify new ZMP by iteratively calculating the best candidate ZMPs to be merged then. In addition, evaluation measures of ZMP are suggested in terms of factors such as coverage, accuracy and a tradeoff of both them. The effectiveness of the proposed approach is demonstrated through a real-world data set obtained, the experiment result shows that the approach can merge the existing zones to discover new ZMP rationally.