Study Of GPS Data De-noising Method Based On Wavelet And Kalman Filtering

There are various error sources playing roles in the impact of GPS positioning accuracy. According to the basic theory of wavelet analysis and GPS signal noise characteristics, this study use the mathematical tool wavelet analysis in GPS data de-noising. Meanwhile, making wavelet analysis combine with Kalman filtering, this adaptive Kalman filtering method based on wavelet analysis is also used in GPS data de-noising. The experimental results show that the method used in GPS data de-noising can achieve good efficiency.The main research contents of this paper are as follows:For the structural characteristics of GPS signals, the study extract the L1, L2 carrier phase data from original signals. In order to weaken the accidental error such as multipath noise and observation noise, choosing one baseline for study object, which is dealed with wavelet de-noising by three schemes such as different decomposition levels, threshold selection methods and wavelet functions. The experimental results show that appropriate choice of de-noising method can greatly improve the accuracy of baseline solution, otherwise the accuracy may be reduced. On the basis of suitable wavelet de-noising method, we process number of baseline observation in different time or length, then provide the effective range of wavelet analysis used in GPS data de-noising.On the basis of wavelet de-noising method, secondary processing several baselines in wavelet de-noising effective range by adaptive Kalman filtering method, then comparing the effect of this new method with which of the single wavelet de-noising method or the single adaptive Kalman filtering method. The experimental results show that the effect of the adaptive Kalman filtering method based on wavelet analysis is better than which of the two single methods. The proportions of three methods improving GPS baseline solution accuracy are 43%, 35%,4%. The adaptive Kalman filtering method based on wavelet analysis used in purifying GPS original observations is the most effective.