An outlier-robust Kalman filter
An outlier-robust Kalman filter
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DOI：
10.1109/icra.2011.5979605
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发表时间：
2011-05
期刊：
影响因子：
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通讯作者：
Gabriel Agamennoni;Juan I. Nieto;E. Nebot
中科院分区：
文献类型：
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作者：
Gabriel Agamennoni;Juan I. Nieto;E. Nebot
我们介绍了一种新的方法来处理序列数据中存在的离群值。我们提出的离群鲁棒卡尔曼滤波器是一个离散时间模型，用于处理含有非高斯和重尾噪声的序列数据。我们提出了有效的滤波和平滑算法，这是直接修改的标准卡尔曼滤波器Rauch-Tung-Striebel递归，但更强大的离群值和异常观测。此外，我们提出了一种以完全无监督的方式学习异常鲁棒卡尔曼滤波器所有参数的算法。我们的方法的潜力证明了在实验中与合成和真实的数据。
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.