Impulsed noise outliers are data points that differs significantly from other observations.They are generally removed from the data set through local regression or Kalman filter algorithm.However, these methods, or their generalizations, are not well suited when the number of outliers is ofthe same order as the number of low-noise data. In this article, we propose a new model for impulsenoised outliers based on simple latent linear Gaussian processes as in the Kalman Filter. We present a fastforward-backward algorithm to filter and smooth sequential data and which also detect these outliers.We compare the robustness and efficiency of this algorithm with classical methods. Finally, we applythis method on a real data set from a Walk Over Weighing system admitting around 60% of outliers. Forthis application, we further develop an (explicit) EM algorithm to calibrate some algorithm parameters.

中文翻译：

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具有脉冲噪声异常值的卡尔曼滤波器：一种鲁棒的顺序算法，用于过滤具有大量异常值的数据

脉冲噪声异常值是与其他观测值有显着差异的数据点。它们通常通过局部回归或卡尔曼滤波器算法从数据集中去除。但是，当异常值的数量相同时，这些方法或它们的泛化不太适合order 作为低噪声数据的数量。在本文中，我们提出了一种基于简单潜在线性高斯过程的脉冲噪声异常值的新模型，如卡尔曼滤波器。我们提出了一种快进-后退算法来过滤和平滑顺序数据，并且还可以检测这些异常值。我们将该算法的鲁棒性和效率与经典方法进行了比较。最后，我们将此方法应用于来自步行称重系统的真实数据集，该系统承认大约 60% 的异常值。对于这个应用程序，