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http://dx.doi.org/10.7848/ksgpc.2015.33.2.123

Modeling of Stochastic Process Noises for Kinematic GPS Positioning  

Chang-Ki, Hong (Dept. of Geoinformatics Engineerig, Kyungil University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.33, no.2, 2015 , pp. 123-129 More about this Journal
Abstract
The Kalman filter has been widely used in the kinematic GPS positioning due to its flexibility and efficiency in computational points of view. At the same time, the relative positioning technique also provided the high precision positioning results by removing the systematic errors in the measurements significantly. However, the positioning quality may be degraded following to longer in baseline length. For this case, it is required that the remaining atmospheric effects, such as double-difference ionospheric delay and zenith wet delay, should be properly modeled by examining the characteristics of the stochastic processes. In general, atmospheric effects are estimated with the assumption of random walk, or the first-order Gauss-Markov stochastic process, which requires the precise modeling on the corresponding process noises. Therefore, we determined and provided the parameters for modelling the process noises for atmospheric effects. The auto-correlation functions are empirically determined at first, and then the parameters are extracted from the empirical auto-correlation function. In fact, the test results can be either applied directly, or used as guidance values for the modeling of process noises in the kinematic GPS positioning.
Keywords
GPS; Kalman Filter; Stochastic Process Noise; Double-difference Ionopsheric Delay; Zenith Wet Delay;
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