In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress h...In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress high-frequency noise,an infinite impulse response filter(IIRF)is introduced at the front end of ASTUKF to preprocess the original data.Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering.After that,the data from the experiment were tested on the hardware experiment platform.The experimental results show that compared to the traditional extended Kalman filter(EKF)and unscented Kalman filter(UKF)algorithms,the root mean square error(RMSE)of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5%and 36.1%;the RMSE of the pitch axis results decreases by nearly 58.4%and 51.5%,respectively;and the RMSE of the yaw axis results decreases almost 62.8%and 50.9%,correspondingly.The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.展开更多
基金supported by the Key Research and Development Program of Shaanxi Province(No.2024NC-YBXM-246)the Shaanxi Provincial Science and Technology Department(No.2024JC-YBQN-0725)+1 种基金the Education Department of Shaanxi Province(No.23JK0371)the Shaanxi University of Technology(No.SLGRCQD2318).
文摘In this paper,an algorithm on measurement noise with adaptive strong tracking unscented Kalman filter(ASTUKF)is advanced to improve the precision of pose estimation and the stability for data computation.To suppress high-frequency noise,an infinite impulse response filter(IIRF)is introduced at the front end of ASTUKF to preprocess the original data.Then the covariance matrix of the error is corrected and the measurement noise is estimated in the process of filtering.After that,the data from the experiment were tested on the hardware experiment platform.The experimental results show that compared to the traditional extended Kalman filter(EKF)and unscented Kalman filter(UKF)algorithms,the root mean square error(RMSE)of the roll axis results from the algorithm proposed in this paper is respectively reduced by approximately 57.5%and 36.1%;the RMSE of the pitch axis results decreases by nearly 58.4%and 51.5%,respectively;and the RMSE of the yaw axis results decreases almost 62.8%and 50.9%,correspondingly.The above results indicate that the algorithm enhances the ability of resisting high-frequency vibration interference and improves the accuracy of attitude solution.
