图 书 馆
学习异常强劲的卡尔曼滤波器Learning an Outlier-Robust Kalman Filter |
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| 课程网址: | http://videolectures.net/ecml07_ting_lor/ |
| 主讲教师: | Jo-Anne Ting |
| 开课单位: | 南加州大学 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this talk, we introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard Kalman filter is not robust to outliers, and other variations of the Kalman filter have been proposed to overcome this issue. However, these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation procedures. Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample. A data sample with a smaller weight has a weaker contribution when estimating the current time step’s state. Using an incremental variational Expectation-Maximization framework, we learn the weights and system dynamics. We evaluate our Kalman filter algorithm on data from a robotic dog. |
| 关 键 词: | 卡尔曼滤波器; 数据处理; 系统动态 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-05：cwx |
| 阅读次数: | 122 |