Performance Analysis of Satellite Clock Bias Based on Wavelet Analysis and Neural Network

In the field of the real-time GPS precise point positioning(PPP),the real-time and reliable prediction of satellite clock bias(SCB) is one key to realize the real-time GPS PPP with high accuracy.The satellite borne GPS atomic clock has high frequency,is very sensitive and extremely easy to be influenced by the outside world and its own factors.So it is very difficult to master its complicated and detailed law of change.With the above characters, a novel four-stage method for SCB prediction based on wavelet analysis and neural network is proposed.The basic ideas,prediction models and steps of clock bias prediction based on wavelet analysis and radial basis function(RBF) network are discussed,respectively. This model adopts "sliding window" to compartmentalize data and utilizes neural network to prognosticate coefficients of clock bias sequence at each layer after wavelet analysis and wiping off noise.As a result,the intricate and meticulous diversification rule of clock bias sequence is obtained more accurately and the clock bias sequence is better approached. Compared with the grey system model and neural network model,a careful precision analysis of SCB prediction is made to verify the feasibility and validity of this proposed method by using the performance parameters of GPS satellite clocks.The simulation results show that the prediction precision of this novel model is much better.This model can afford the SCB prediction with relatively high precision for real-time GPS PPP.