Cascaded Kalman Filtering-Based Attitude and Gyro Bias Estimation With Efficient Compensation of External Accelerations
Muhammad Azeem Javed, Muhammad Tahir, Khurram Ali
Abstract
We consider the problem of attitude estimation of rigid bodies in motion using low cost inertial measurement unit (IMU). An efficient scheme is proposed using two different Kalman filters by deriving their measurement models for precise attitude (pitch and roll) estimation in the presence of high and prolonged dynamic conditions and gyro bias. Both filters work in a coupled fashion where one of the filters provides accurate estimates of rigid body attitude and external acceleration using the accelerometer in conjunction with the gyroscope while the second filter is responsible to estimate the gyro bias, allowing the proposed scheme to be used in any application with minimal calibration. A new threshold based external acceleration detection module is also introduced to change the confidence level on external acceleration prediction to assist the estimation process. The proposed scheme is tested and compared with other existing estimators in the literature under different dynamical conditions and real-world experimental data sets in order to validate its effectiveness.