High Dynamic Gps Positioning Filter Algorithm Simulation Study

Global Positioning System (GPS) is a satellite navigation system designed to provide instantaneous three-dimensional position information almost anywhere on the globe at any time, and in any weather. It have greatly applied in many areas such as aeronautics, astronautics, marine and other civil fields. The aircraft have high dynamics in aeronautic and astronautic fields, and there are many random errors in the high dynamic GPS positioning data. With the measurement series for GPS and establishing a kinematic model to describe the motion of the user, the errors will be decreased and the positioning precision will be increased when applying the kalman filtering to GPS positioning computation model. However, we have so great difficulty in accurately describing the state of the system that we barge up against some problem such as filtering divergence and computational divergence. The article puts forward three different model on the foundation of analyzing error's reasons, they are the general model, the deviate adapted model and the adapted model with Doppler shift. The article obtains the results of computer simulation and presents the practicable solutions for the problems occurring in kalman filtering. The results present that the algorithms of positioning and filtering are fit for navigation of high dynamic GPS user. The dynamic performance of the filter are bad and the positioning precision is low when GPS filter with the general model. The dynamic performance and the positioning precision will be improved when filtering with the deviate adapted model. The filter's dynamic performance will be very ameliorated and the precision of positioning is greatly increased when GPS filter with the adapted model with Doppler shift.