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.

翻译：隐性噪声外源是与其他观测结果大相径庭的数据点。 它们一般从本地回归或Kalman过滤算法中的数据组中移除。 但是, 当外源的数量与低噪音数据的数量顺序相同时, 这些方法或其概括性并不十分合适。 在本篇文章中, 我们建议了基于像 Kalman 过滤器那样的简单潜线性线性高斯进程而实现的脉冲外源新模式。 我们向后推算法到过滤法和平稳的顺序数据, 并且也可以检测这些外源数据。 我们用经典方法比较了此算法的坚固性和效率。 最后, 我们将这种方法应用在“ 走过宽”系统设定的真实数据上, 接受大约60%的外源。 为此, 我们进一步开发了一种( 直观的) EM 算法来校准某些算法参数 。