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An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot
Alternative TitleAn Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot
SONG Qi; HAN JianDa
2008
Source Publication自动化学报
ISSN0254-4156
Volume000Issue:1.0Pages:72-79
AbstractFor improving the estimation accuracy and the convergence speed of the unscented Kalman filter (UKF), a novel adaptive filter method is proposed. The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function. On the basis of the MIT rule, an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function. The updated covariance is fed back into the normal UKF. Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations. The asymptotic properties of this adaptive UKF are discussed. Simulations are conducted using an omni-directional mobile robot, and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.
Other AbstractFor improving the estimation accuracy and the convergence speed of the unscented Kalman filter (UKF), a novel adaptive filter method is proposed. The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function. On the basis of the MIT rule, an adaptive algorithm is designed to update the covariance of the process uncertainties online by minimizing the cost function. The updated covariance is fed back into the normal UKF. Such an adaptive mechanism is intended to compensate the lack of a priori knowledge of the process uncertainty distribution and to improve the performance of UKF for the active state and parameter estimations. The asymptotic properties of this adaptive UKF are discussed. Simulations are conducted using an omni-directional mobile robot, and the results are compared with those obtained by normal UKF to demonstrate its effectiveness and advantage over the previous methods.
Keyword卡尔曼滤波器算法 移动式遥控装置 状态估计 参数估计 过程协方差
Indexed ByCSCD
Language中文
CSCD IDCSCD:3270063
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imr.ac.cn/handle/321006/148222
Collection中国科学院金属研究所
Affiliation中国科学院金属研究所
Recommended Citation
GB/T 7714
SONG Qi,HAN JianDa. An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot[J]. 自动化学报,2008,000(1.0):72-79.
APA SONG Qi,&HAN JianDa.(2008).An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot.自动化学报,000(1.0),72-79.
MLA SONG Qi,et al."An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot".自动化学报 000.1.0(2008):72-79.
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