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Impulsed noise outliers are data points that differs significantly from otherobservations.They are generally removed from the data set through localregression or Kalman filter algorithm.However, these methods, or theirgeneralizations, are not well suited when the number of outliers is ofthe sameorder as the number of low-noise data. In this article, we propose a new modelfor impulsenoised outliers based on simple latent linear Gaussian processes asin the Kalman Filter. We present a fastforward-backward algorithm to filter andsmooth sequential data and which also detect these outliers.We compare therobustness and efficiency of this algorithm with classical methods. Finally, weapplythis method on a real data set from a Walk Over Weighing system admittingaround 60% of outliers. Forthis application, we further develop an (explicit)EM algorithm to calibrate some algorithm parameters.

Kalman filter with impulse noised outliers : A robust sequential algorithm to filter data with a large number of outliers