GPS Positioning Method Based on Kalman Filtering

Abstract

Global positioning system means a satellite-based radio navigation system, which plays an important role in many areas. The Kalman filter can be exploited to estimate states of dynamic systems via a stochastic linear state-space model. However, the Kalman filter may diverge easily when the forecast model cannot estimate the state vector values accurately. In order to control the degree of divergence, an adaptive approach should be studied. Therefore, we propose an adaptively robust Kalman filter with an adaptive factor can effectively control errors in measurements, with both the information from measurements and the information from the proposed prediction model. To test the effectiveness of the proposed method, position and velocity estimation error rate of the proposed GPS positioning system are tested, and experimental results demonstrated that the proposed method can provide high quality GPS positioning service.