To improve the accuracy of attitude and heading reference systems for moving vehicles, an effective orientation estimation method is proposed. The method uses an odometer, a low-cost magnetic, angular rate, and gravity sensor. This study addresses the problems of non-orthogonal error, carrier magnetic field interference and calibration to obtain accurate, long-term, stable magnetic field strength. A neural network fusion 12-parameter ellipse fitting method is proposed to eliminate the soft magnetic field and hard magnetic field interference. The interference to the accelerometer from linear acceleration is eliminated by using an odometer and a gyroscope, and the high-frequency noise from the accelerometer is eliminated by using a low-pass filter. An improved method to evaluate vehicle attitude is proposed and utilized to compensate for filtered accelerometer measurement when the vehicle is moving at a uniform, accelerate and steering state. The proposed method uses an effective adaptive Kalman filter based on the error state model to reduce dynamic perturbations. Filter gain is adaptively tuned under different moving modes by adjusting the noise matrix. The effectiveness of the algorithm is verified by experiments and simulations in multiple operating conditions.
A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a land vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement unit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and magnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system is designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system will be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1°. Finally, an outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer. The experimental results show the enhancement in restraining observation outliers that improves the precision of the integrated navigation system.
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