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Comparing stochastic models used in GPS precise point positioning technique

Abstract GPS observables are normally processed using the least-squares method. In order to ensure high accuracy positioning results, both the functional model and the stochastic model must be correctly defined. In GPS Precise Point Positioning (PPP) mode, an ionosphere-free linear combination is generally used for constructing the functional mode, while post-mission information and error mitigation methods are employed to eliminate many biases. Nevertheless, some unmodelled biases still remain in the GPS observables and eventually cause errors in the coordinate results. It is, however, possible to further improve the accuracy and reliability of GPS results through an enhancement of the stochastic model. This research aims to investigate stochastic models used in the GPS PPP technique. Three different stochastic models; equal weight for all GPS observables, different weight for each GPS observable based on satellite elevation angle and different weight for each GPS observable based on the MINQUE (Minimum Norm Quadratic Unbiased Estimation) procedure, have been compared in this paper. Test results indicate that the stochastic model estimated from the MINQUE method produces the most accurate coordinate results both in horizontal and vertical components.