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Cyril Falcon

Publications and source records attributed to Cyril Falcon.

2 recordsLinked to original sources
arXiv2025

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

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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arXiv2025

Recursive KalmanNet: Deep Learning-Augmented Kalman Filtering for State Estimation with Consistent Uncertainty Quantification

State estimation in stochastic dynamical systems with noisy measurements is a challenge. While the Kalman filter is optimal for linear systems with independent Gaussian white noise, real-world conditions often deviate from these assumptions, prompting the rise of data-driven filtering techniques. This paper introduces Recursive KalmanNet, a Kalman-filter-informed recurrent neural network designed for accurate state estimation with consistent error covariance quantification. Our approach propagates error covariance using the recursive Joseph's formula and optimizes the Gaussian negative log-likelihood. Experiments with non-Gaussian measurement white noise demonstrate that our model outperforms both the conventional Kalman filter and an existing state-of-the-art deep learning based estimator.

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