| 一种用于移动机器人状态和参数估计的自适应UKF算法 |
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| 引用本文： | 宋崎,韩建达.一种用于移动机器人状态和参数估计的自适应UKF算法[J].自动化学报,2008,34(1). |
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| 作者姓名： | 宋崎 韩建达 |
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| 作者单位： | 1，2 |
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| 基金项目： | Supported by National High Technology Research and Development Program of China(863 Program)，Hi-Tech Research and Development Program of China(2003AA421020) |
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| 摘 要： | For 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.
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| 关 键 词： | Adaptive Unscented Kalman filter (UKF) innovation MIT rule process covariance 移动机器人 状态 参数估计 自适应 算法 Mobile Robot Parameter State Algorithm results effectiveness advantage methods Simulations mobile robot asymptotic properties adaptive mechanism compensate lack knowledge |
An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot |
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| SONG Qi,HAN Jian-Da.An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot[J].Acta Automatica Sinica,2008,34(1). |
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| Authors: | SONG Qi HAN Jian-Da |
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| Abstract: | For 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. |
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| Keywords: | Adaptive Unscented Kalman filter(UKF) innovation MIT rule process covariance |
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