A novel fusion methodology to bridge GPS outages for land vehicle positioning

Many intelligent transportation system applications require accurate, reliable, and continuous vehicle position information whether in open-sky environments or in Global Positioning System (GPS) denied environments. However, there remains a challenging task for land vehicles to achieve such positioning performance using low-cost sensors, especially microelectromechanical system (MEMS) sensors. In this paper, a novel and cost-effective fusion methodology to bridge GPS outages is proposed and applied in the Inertial Navigation System (INS)/GPS/compass integrated positioning system. In the implementation of the proposed methodology, a key data preprocessing algorithm is first developed to eliminate the noise in inertial sensors in order to provide more accurate information for subsequent modeling. Then, a novel hybrid strategy incorporating the designed autoregressive model (AR model)-based forward estimator (ARFE) with Kalman filter (KF) is presented to predict the INS position errors during GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road tests with various scenarios were performed. The proposed methodology illustrates significant improvement in positioning accuracy during GPS outages.