| 基于Cauchy鲁棒函数的UKF改进算法 |
| |
| 引用本文： | 高丽君,刘济.基于Cauchy鲁棒函数的UKF改进算法[J].医学教育探索,2015(2):244-249. |
| |
| 作者姓名： | 高丽君 刘济 |
| |
| 作者单位： | 华东理工大学信息科学与工程学院, 上海 200237,华东理工大学信息科学与工程学院, 上海 200237 |
| |
| 基金项目： | 上海市自然科学基金（11ZR1409800） |
| |
| 摘 要： | 对于大多数实际系统，其噪声统计特性未知，不敏卡尔曼滤波(unscented Kalman filter,UKF)算法对噪声信息不准的鲁棒性较差，导致滤波精度急剧下降，甚至滤波发散。借助鲁棒数据校正的思想，提出了一种基于Cauchy鲁棒函数的UKF改进算法。以UKF的测量先验值与其实际值的残差作为基准，采用联合权函数对噪声估计值进行实时修正，从而提高了UKF算法的精度。通过两个实例的仿真，验证该算法的有效性。
|
| 关 键 词： | 噪声估计不准 不敏卡尔曼滤波 Cauchy鲁棒函数 |
| 收稿时间： | 2014/6/18 0:00:00 |
Improved UKF Algorithm Based on Cauchy Robust Function |
| |
| GAO Li-jun and LIU Ji.Improved UKF Algorithm Based on Cauchy Robust Function[J].Researches in Medical Education,2015(2):244-249. |
| |
| Authors: | GAO Li-jun and LIU Ji |
| |
| Institution: | School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China and School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China |
| |
| Abstract: | The statistics of noise are usually unknown in most actual systems. Moreover, it is known that the Unscented Kalman Filter (UKF) has poor robustness on the inaccurate noise information, which also makes the filter accuracy rapidly decrease, even diverge. By using the robust correction of data, this work proposes an improved UKF algorithm based on Cauchy Robust function. The noise estimation is adaptively corrected by the combining weighted function, which is calculated from the residual error between process measurement and its estimation. Thus, the accuracy of the UKF algorithm can be increased. Finally, two simulation examples are provided to show the effectiveness of the proposed algorithm. |
| |
| Keywords: | inaccurate noise estimation unscented Kalman filter(UKF) Cauchy robust function |
|
| 点击此处可从《医学教育探索》浏览原始摘要信息 |
| 点击此处可从《医学教育探索》下载免费的PDF全文 |
|
