Research on a mixed prediction method to vehicle integrated navigation systems

Abstract

Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.