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| Record Number | 10118 |
| Reference Type | Report |
| Author(s) | Ting, J.; Theodorou, E.; Schaal, S. |
| Year | 2007 |
| Title | Learning an Outlier-Robust Kalman Filter |
| Journal/Conference/Book Title | CLMC Technical Report: TR-CLMC-2007-1 |
| Keywords | automatic outlier detection, kalman filter, system dynamics, weighted least squares, bayesian statistical learning |
Abstract |
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.
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| Notes | clmc |
| Link to PDF | http://www-clmc.usc.edu/publications//T/TR-CLMC-2007-1.pdf |
| Place Published | Los Angeles, CA |
| Type of Work | CLMC Technical Report |
| Research Notes | A longer and more complete version of ECML paper by Ting, Theodorou & Schaal (2007), with the same title. |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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Page last modified on August 10, 2006, at 06:47 PM
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