| Selection of noise parameters for Kalman filter |
| |
| 作者姓名： | 阮家荣 许嘉贤 莫启明 |
| |
| 作者单位： | Department of Civil and Environmental Engineering, University of Macau, China |
| |
| 基金项目： | Supported by： Fundo para o Desenvolvimento das Ciencias e da Tecnologia （FDCT） Under Grant No. 052/2005/A Acknowledgments The generous financial support from the Fundo para o Desenvolvimento das Ciencias e da Tecnologia （FDCT） under grant 052/2005/A is gratefully acknowledged. |
| |
| 摘 要： | The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likelihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation.
|
| 关 键 词： | 卡尔曼滤波器 噪声参数 选择 状态估计 |
| 文章编号： | 1671-3664（2007）01-0049-08 |
| 收稿时间： | 2006-10-10 |
| 修稿时间： | 2006-11-03 |
Selection of noise parameters for Kalman filter |
| |
| Ka-Veng?Yuen,Ka-In?Hoi,Kai-Meng?Mok.Selection of noise parameters for Kalman filter[J].Earthquake Engineering and Engineering Vibration,2007,6(1):49-56. |
| |
| Authors: | Ka-Veng Yuen Ka-In Hoi Kai-Meng Mok |
| |
| Institution: | Department of Civil and Environmental Engineering, University of Macau, China |
| |
| Abstract: | The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likklihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation. |
| |
| Keywords: | Bayesian inference Kalman filter measurement noise process noise state estimation |
| 本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录！ |
