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 Agamennoni, G., Nieto, J.I. & Nebot, E. |
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An outlier-robust kalman filter
Proceedings of the 2011 IEEE International Conference on Robotics and Automation, pp. 1551-1558, May, 2011
Presented at 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 09 May. - 13 May. 2011
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Abstract
We introduce a novel approach for processing sequential data in the presence of outliers. The outlier-robust Kalman filter we propose is a discrete-time model for sequential data corrupted with non-Gaussian and heavy-tailed noise. We present efficient filtering and smoothing algorithms which are straightforward modifications of the standard Kalman filter Rauch-Tung-Striebel recursions and yet are much more robust to outliers and anomalous observations. Additionally, we present an algorithm for learning all of the parameters of our outlier-robust Kalman filter in a completely unsupervised manner. The potential of our approach is borne out in experiments with synthetic and real data.
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