Accurate tracking for augmented reality applications is a challenging task. Multi-sensor hybrid tracking generally provides more stable resalts than single visual tracking. A new tightly-coupled hybrid tracking approach combining vision-based systems with an inertial sensor is presented in this paper. Based on the multi-frequency sampling theory in the measurement data synchronization

a strong tracking filter is used to smooth sensor data and estimate the position and orientation. Through adding a time-varying fading factor to adaptively adjust the prediction error covariance of the filter

this method improves the performance of tracking for fast moving targets. Experimental results with occluded markers show that proposed approach can effectively improve the prediction accuracy of location information to target motion with the hybrid tracking algorithm based on the extended Kalman filter

improve the stability of fast moving \r\ntarget tracking. Our approach is suitable for a large range of mobile conditions.