Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System

Abstract

The extended Kalman filter (EKF) as a primary integration scheme has been applied in the Global Positioning System (GPS) and inertial navigation system (INS) integrated system. Nevertheless, the inherent drawbacks of EKF contain not only instability caused by linearization, but also massive calculation of Jacobian matrix. To cope with this problem, the adaptive interacting multiple model (AIMM) filter method is proposed to enhance navigation performance. The soft-switching characteristic, which is provided by interacting multiple model algorithm, permits process noise to be converted between upper and lower limits, and the measurement covariance is regulated by Sage adaptive filtering on-line Moreover, since the pseudo-range and Doppler observations need to be updated, an updating policy for classified measurement is considered. Finally, the performance of the GPS/INS integration method on the basis of AIMM is evaluated by a real ship, and comparison results demonstrate that AIMM could achieve a more position accuracy.