Estimating Vehicle State by GPS/IMU Fusion with Vehicle Dynamics

In this paper, a low-cost navigation system with high integrity and reliability is proposed. A high-integrity estimation filter is proposed to obtain a high-accuracy state estimate. The filter utilizes a vehicle velocity constraint measurement to enhance the accuracy of the estimate. Two estimation filters, the extended Kalman filter (EKF) and the extended information filter (EIF), are designed and compared to obtain the estimate of the vehicle state. An instrumentation system that consists of a microcontroller, GPS receiver, IMU, velocity encoder, and Zigbee transceiver is used. The microcontroller provides a vehicle navigation solution at 50 Hz by fusing the measurements of the IMU and GPS receiver using the proposed filter design. Extensive experimental tests are conducted to verify the accuracy of the proposed algorithm. These results are processed with and without the velocity constraints. The estimation accuracy improvement with the addition of the velocity constraints is shown. A more than 16 % reduction in the computational time is demonstrated when using the EIF in comparison to the EKF approach.