Geodetic Data Processing Center

Dabagou, China

Geodetic Data Processing Center

Dabagou, China
SEARCH FILTERS
Time filter
Source Type

Nie J.,Geodetic Data Processing Center | Nie J.,Chang'an University | Yang Y.,Xi'an Jiaotong University | Wu F.,Xi'an Jiaotong University | Wu F.,Zhengzhou University
Acta Geodaetica et Cartographica Sinica | Year: 2010

A modified particle filtering is proposed. The convergence speed of the particle filtering is tried to be improved. The influences of linearization of nonlinear functional models and the non-Gaussian random errors to the results of dynamic precise point positioning will be weakened. In the new procedure, the free-ionosphere ambiguities are fixed at first to reduce the number of parameters in the state vector. The accuracy of the initial positioning results is improved and the convergence of the particle filtering is modified. Kalman filtering as predicted filtering of particle filtering is employed to improve the efficiency of the important sampling of the particle filtering and the precision of the sampling particles, as well as to slow down the degeneracy of the particle. An actual dynamic GPS data set is employed to test the new particle filtering procedure. It is shown that the modified procedure of the particle filtering based on fixing free-ionosphere ambiguities can improve the accuracy of the dynamic precise point positioning.


He Z.,Chang'an University | Nie J.,Geodetic Data Processing Center | Wu F.,Zhengzhou University | Wu F.,Xian Research Institute of Surveying and Mapping | And 2 more authors.
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2012

Kalman filter is widely used in the area of kinematic positioning and navigation. However, it doesn't have the ability to resist the influence of measurement outliers, hence its performance is easy impacted by the observation outliers or kinematic state disturbing. In order to guarantee the reliability of the navigation with precise dynamic model, a model set, which contains many different observation models, is established. An improved Kalman filtering, in which the design matrix of the observational model is substituted by its expectation is proposed to control the influences of the measurement outliers. An integrated GPS/INS navigation example is given to show that the modified Kalman filtering algorithm works well.


Ke B.,Chinese Academy of Surveying and Mapping | Zhang C.,Chinese Academy of Surveying and Mapping | Guo C.,Geodetic Data Processing Center | Wang B.,Geodetic Data Processing Center | Yang L.,State Oceanic Administration
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2015

The characteristics of shipborne gravimetric data from offshore China were analysed, and a method to eliminate systematic deviation in the gravity for different survey lines located in different regions is also proposed. A criteria for determining shipborne gravimetric survey lines for base lines is presented, and the residual disturbance gravity at the cross point between two different survey lines on the base line was used to rectify the other values for different shipborne gravimetric survey lines. These experiemental results show that, in accordance with the criteria presented, when correcting the gravity of the survey line in the proposed method the gravity value difference on different lines does not include a systematic bias, the intersections of discrepancies significantly improved, and the survey lines to correct the gravity disturbance were significantly better than those without pre-corrected gravity disturbance. ©, 2015, Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University. All right reserved.


Wu F.,Zhengzhou University | Wu F.,Xian Research Institute of Surveying and Mapping | Nie J.,Geodetic Data Processing Center | He Z.,Chang'an University
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2012

In GPS/INS integrated navigation, the number of observations is usually less than that of the state parameters, and the single adaptive factor is usually applied in Kalman filtering, which can lead to precision loss of indirect observational parameters. A new algorithm of classified adaptive filtering is presented based on predicted residuals and selecting weight filtering, and the corresponding formulas are given. Finally, an actual calculation is given. The new algorithm can not only degrade the influence of the disturbances from the state but also avoid the loss of estimated precision of indirect observational parameters, and improve the accuracy of the navigation further.


He Z.,Chang'an University | Wu F.,Zhengzhou University | Wu F.,Xian Research Institute of Surveying and Mapping | Nie J.,Geodetic Data Processing Center
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2011

The influence of the prior covariance errors to the standard dynamic Kalman filtering is discussed. The influence expressions of the prior covariance matrix errors including the covariance matrix of state parameters, dynamical model errors and measurement noises are deduced. A GPS/INS tight integration navigation is performed, and it shows that the unreasonable errors of the covariance matrices of the dynamic model information and measurements will result in biases of the dynamic navigation results. The minus errors of the covariance matrix of dynamical model information will increase the navigation errors. If the errors of the covariance matrix of the predicted states are positive, then the effects of the dynamical model error will be weakened. However, contrary conclusion could be got if only the errors of the covariance matrix of measurement noises are considered.


Juqing Z.,Chang'an University | Jianliang N.,Geodetic Data Processing Center | Yafang X.,Chang'an University
Proceedings - 4th International Conference on Intelligent Computation Technology and Automation, ICICTA 2011 | Year: 2011

Due to different levels and characteristic deformation existing in any images, it is difficult to select an appropriate model to correct effectively. Considering BP neural network is a high and nonlinear complicated system which can approach at any precision, we attempt to use it for image geometric correction. In this paper, errors correction of scanning map by BP neural network as an example was discussed. The result shows that the accuracy of scanning map corrected by the BP neural network is higher than by normal function fitting. © 2011 IEEE.


Nie J.,Geodetic Data Processing Center | Wu F.,Xian Research Institute of Surveying and Mapping | Guo C.,Geodetic Data Processing Center | Cheng C.,Geodetic Data Processing Center
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2012

The precision of dynamic precise point positioning using Kalman filtering will be degraded, even be divergent when the outliers exist. Particle filtering is applied to control the influences of the observational outliers, and improve the accuracy of positioning. Particle filtering is a kind of nonlinear filter with non-Gaussian distribution, and it can obtain accurate parameters by random sample. The weight of each particle is defined based on the probability densities of the observational errors, predicted state errors as well as the important distribution in order to control the influences of contaminated particles to the positioning results. Kalman filtering is employed to get the important sampling to slow down the degeneracy of the particle. The free-ionosphere ambiguities are fixed before data processing to reduce the number of parameters in the state vector. An actual dynamic GPS data set is employed to test the particle filter procedure. The procedure of the particle filtering can efficiently control the influences of the observational outliers, and improve the accuracy of the dynamic precise point positioning.


Wang W.,Geodetic Data Processing Center | Cheng C.,Geodetic Data Processing Center | Chen J.,Geodetic Data Processing Center
Journal of Geomatics | Year: 2010

We introduce various coordinate transformation models and their practicability. Through a large of tentative calculations, we analze the coincident point density influence in different coordinate transformation model and four-parameter model of two-dimensional projection deformation, we study size of different coordinate transformation model of extrapoled extension and extrapoled error, and obtain corresponding conclusion that provides useful of reference for different regions of geographic information coordinate transformation.


Cheng C.,Geodetic Data Processing Center | Jiang G.,Geodetic Data Processing Center | Nie J.,Geodetic Data Processing Center | Tian X.,Geodetic Data Processing Center
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2014

In connection with huge GNSS network data processing, has three strategy approches, a whole huge net data process, a non-difference method and a divide the network to small networks process, we propose to divide a network and analyse the limitations of this module based on the GAMIT subnet module and present a move grid density method. The method is researched and analyzed in detail using data from CMONOC. The results indicate that the baseline repeatability and site precision is more than methods based on the GAMIT subnet module method's result, while the algorithm weakens the unreasonable situation in subnetting, enhancing the reliability of a subnet structure.


Guo C.,Geodetic Data Processing Center | Nie J.,Geodetic Data Processing Center | Wang B.,Geodetic Data Processing Center | Jiang G.,Geodetic Data Processing Center
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2013

The efficient fitting way is the key to improve the accuracy of refining quasigeoid during the refinement. Collocation is used to control the influence of systematic errors in the paper for there are systematic errors in the difference between GPS/level and gravity quasigeoid. For covariance matrices of the signal vector and the observational noise are not harmonic in least square collocation, the covariance matrices are adaptively adjusted to approach the true with estimation of variance components in order to improve the accuracy of quasigeoid. An example of one eastern city with the area of 20 000 km2 is given. It is shown that least square collocation and adaptive least square collocation can heighten the reliability of fitting, make the accuracy of quasigeoid higher and get the 1cm accuracy quasigeoid of large area in China.

Loading Geodetic Data Processing Center collaborators
Loading Geodetic Data Processing Center collaborators