Research on a mixed prediction method to vehicle integrated navigation systems

Di Zhao,Huaming Qian,Dingjie Xu

doi:10.1177/1729881419885258

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.

Highlights

The vehicle integrated navigation systems commonly consist of two navigation technologies: strapdown inertial navigation system (SINS) and Global Positioning System (GPS)
In order to investigate the effect of information fusion method with different locked-out time, five GPS outages are selected in the whole trajectory, the GPS blocked time span is set 50 s, 100 s, 300 s, 500 s, 800 s, respectively
The errors of unscented Kalman filter (UKF) method will accumulate with time growing, and the rapidly increasing errors make the accuracy of navigation system decrease, especially in long-term GPS outages (300 s, 500 s, 800 s)

Summary

Introduction

The vehicle integrated navigation systems commonly consist of two navigation technologies: strapdown inertial navigation system (SINS) and Global Positioning System (GPS). Aiming to provide good performance to the navigation system, this article propose a hybrid prediction method to assist unscented Kalman filter (UKF) when GPS signal temporary outages occur. When GPS outages happen, the prediction of position and velocity errors by both RBFNN and TSA are provided to correct SINS errors, make sure the good performance of navigation system. This method has been simulated and verified on MATLAB by using vehicle simulation data. The operational process of the proposed model is trained RBFNN and TSA when GPS signal is available, and use the predict value of both RBFNN and TSA to compensating the errors of integration navigation system when GPS outages. The functional block diagram of the proposed method during GPS outages is shown as Figure 4

Experimental setup

Conclusion