KALMAN FILTER WITH IMPULSE NOISED OUTLIERS : A ROBUST SEQUENTIAL ALGORITHM TO FILTER DATA WITH A LARGE NUMBER OF OUTLIERS - Archive ouverte HAL
Pré-Publication, Document De Travail Année : 2022

KALMAN FILTER WITH IMPULSE NOISED OUTLIERS : A ROBUST SEQUENTIAL ALGORITHM TO FILTER DATA WITH A LARGE NUMBER OF OUTLIERS

Résumé

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 of the same order as the number of low-noise data. In this article, we propose a new model for impulse noised outliers based on simple latent linear Gaussian processes as in the Kalman Filter. We present a fast forward-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 apply this method on a real data set from a Walk Over Weighing system admitting around 60% of outliers. For this application, we further develop an (explicit) EM algorithm to calibrate some algorithm parameters.
Fichier principal
Vignette du fichier
main_hal.pdf (550.35 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03740301 , version 1 (29-07-2022)

Identifiants

Citer

Bertrand Cloez, Bénédicte Fontez, Eliel González García, Isabelle Sanchez. KALMAN FILTER WITH IMPULSE NOISED OUTLIERS : A ROBUST SEQUENTIAL ALGORITHM TO FILTER DATA WITH A LARGE NUMBER OF OUTLIERS. 2022. ⟨hal-03740301⟩
54 Consultations
105 Téléchargements

Altmetric

Partager

More