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arXiv · 2507.14144

Recursive KalmanNet: Analyse des capacit\'es de g\'en\'eralisation d'un r\'eseau de neurones r\'ecurrent guid\'e par un filtre de Kalman

Cyril Falcon·Hassan Mortada·Mathéo Clavaud·Jean-Philippe Michel
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Abstract

The Recursive KalmanNet, recently introduced by the authors, is a recurrent neural network guided by a Kalman filter, capable of estimating the state variables and error covariance of stochastic dynamic systems from noisy measurements, without prior knowledge of the noise characteristics. This paper explores its generalization capabilities in out-of-distribution scenarios, where the temporal dynamics of the test measurements differ from those encountered during training. Le Recursive KalmanNet, r\'ecemment introduit par les auteurs, est un r\'eseau de neurones r\'ecurrent guid\'e par un filtre de Kalman, capable d'estimer les variables d'\'etat et la covariance des erreurs des syst\`emes dynamiques stochastiques \`a partir de mesures bruit\'ees, sans connaissance pr\'ealable des caract\'eristiques des bruits. Cet article explore ses capacit\'es de g\'en\'eralisation dans des sc\'enarios hors distribution, o\`u les dynamiques temporelles des mesures de test diff\`erent de celles rencontr\'ees \`a l'entra\^inement.

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Cyril Falcon, Hassan Mortada, Mathéo Clavaud, Jean-Philippe Michel. 2025-06-25. Recursive KalmanNet: Analyse des capacit\'es de g\'en\'eralisation d'un r\'eseau de neurones r\'ecurrent guid\'e par un filtre de Kalman. https://arxiv.org/abs/2507.14144

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