Chaotic Characteristic Analysis and Prediction of GPS Static Point Positioning Error

The conventional prediction model of GPS static point positioning (GSPP) system is usually considered that the positioning errors are caused by the external stochastic factors. And many independent models were built for every single type of errors. Actually, the observed error time series is mostly a seemingly random nonlinear chaotic series. In this paper, phase space reconstruction and chaotic characteristic analysis were applied to the error time series of GSPP system. By calculating the largest Lyapunov exponent, all these time series were estimated as chaotic time series. By this conclusion, a method of error prediction based on the largest Lyapunov exponent was proposed to predicting the static point positioning error. The results show that this prediction method has a high prediction precision and it can reduce the complexity of prediction model effectively.