Kalman filter with impulse noised outliers : A robust sequential algorithm to filter data with a large number of outliers
Abstract
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.
Keywords
Cite
@article{arxiv.2208.00961,
title = {Kalman filter with impulse noised outliers : A robust sequential algorithm to filter data with a large number of outliers},
author = {Bertrand Cloez and Bénédicte Fontez and Eliel González García and Isabelle Sanchez},
journal= {arXiv preprint arXiv:2208.00961},
year = {2022}
}