An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastie model. The adaptive filter has six states with a time variable transition matrix. ...An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastie model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good eslimates of the states. When the system is in the high dynamic mode and the bias has converged to an aceurate estimate, the attitude caleulation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance,展开更多
To improve the accuracy of the calculation of a heading angle under magnetic interference,magnetometers and inertial measurement units(IMUs)were fused.The observation value of the heading angle was deduced on the basi...To improve the accuracy of the calculation of a heading angle under magnetic interference,magnetometers and inertial measurement units(IMUs)were fused.The observation value of the heading angle was deduced on the basis of the modeling of the magnetometer error and the analysis of the relation of the magnetometer triaxial output and the distribution characteristics of the magnetic field at two adjacent time periods.Meanwhile,the gyro state and angular velocity increment were used as the basis of the IMU to calculate the prediction value of the heading angle.With the changes in the heading angle and environmental interference,a random forest(RF)algorithm was used to iteratively calculate the weights to fuse the observation value of the heading angle based on the magnetometer and the prediction value of the heading angle based on the IMU.The results show that relative to the common sensor fusion method,the proposed sensor fusion method based on the RF algorithm achieved an approximate 60%improvement in heading angle accuracy.Hence,the proposed method can effectively improve the accuracy of the heading angle under magnetic interference by using an RF algorithm to calculate the output weights of the magnetometer and IMU.展开更多
文摘An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastie model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good eslimates of the states. When the system is in the high dynamic mode and the bias has converged to an aceurate estimate, the attitude caleulation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance,
基金The National Natural Science Foundation of China(No.51708299).
文摘To improve the accuracy of the calculation of a heading angle under magnetic interference,magnetometers and inertial measurement units(IMUs)were fused.The observation value of the heading angle was deduced on the basis of the modeling of the magnetometer error and the analysis of the relation of the magnetometer triaxial output and the distribution characteristics of the magnetic field at two adjacent time periods.Meanwhile,the gyro state and angular velocity increment were used as the basis of the IMU to calculate the prediction value of the heading angle.With the changes in the heading angle and environmental interference,a random forest(RF)algorithm was used to iteratively calculate the weights to fuse the observation value of the heading angle based on the magnetometer and the prediction value of the heading angle based on the IMU.The results show that relative to the common sensor fusion method,the proposed sensor fusion method based on the RF algorithm achieved an approximate 60%improvement in heading angle accuracy.Hence,the proposed method can effectively improve the accuracy of the heading angle under magnetic interference by using an RF algorithm to calculate the output weights of the magnetometer and IMU.
