The information of the road grade plays an important role in improving the ride comfort and fuel consumption. This paper proposes a Quaternion Unscented Kalman Filter (QUKF) to estimate the road grade accurately, which needs only measurements from low-cost Inertial Measurement Unit (IMU). The model is built based on the data from accelerometer and gyroscope. The quaternion, which represents orientations and rotations, is chosen to be the state variables, while the three-axle acceleration is set as measurement vector. The proposed observer is tested and verified using the simulation software CarSim and MATLAB Simulink under several scenarios. To compare the performance of the algorithm, the Kalman filter and complementary filter are also implemented under the same simulation conditions. The results illustrate that the presented observer improves the accuracy and stability. Finally, the results of experiments are delivered and the performance of the filter is assessed against the output of a complete GPS/INS available in the same real-world dataset.


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    Title :

    A Quaternion Unscented Kalman Filter for Road Grade Estimation


    Contributors:
    He, Wenpei (author) / Xi, Junqiang (author)


    Publication date :

    2020-10-19


    Size :

    1563145 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






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