arXiv AI June 2, 2026 By Adnan Harun Dogan, Berken Utku Demirel, Christian Holz

FW-NKF: Frequency-Weighted Neural Kalman Filters

arXiv:2606. 02251v1 Announce Type: cross Abstract: Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch such as sensor vibrations, electromagnetic interference, and periodic noise.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 15

Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators

arXiv:2606. 14195v1 Announce Type: new Abstract: Kalman filters based on the Embedded Latent Transfer Operators (ELTO) emerge as novel statistical tools for sequential state estimation.

By Naichang Ke, Pongpisit Thanasutives, Yoshinobu Kawahara

arXiv AI

Jun 30

Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter

arXiv:2606. 28441v1 Announce Type: cross Abstract: Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for diverse applications, including sequential decision making, anomaly and change-point detection.

By George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos

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arXiv Machine Learning

Jun 15

Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems

arXiv:2606. 14079v1 Announce Type: new Abstract: We propose a spectral learning method for stochastic nonlinear dynamical systems represented with embedded latent transfer operators in deep feature spaces.

By Ryogo Tanaka, Yoshinobu Kawahara

arXiv Machine Learning

Aug 6

Unscented KalmanNet: a hybrid deep learning filter with calibrated posterior covariance for nonlinear state estimation

arXiv:2608. 04201v1 Announce Type: new Abstract: State estimation for nonlinear dynamical systems is commonly performed with the Unscented Kalman filter (UKF), which propagates the state moments through deterministic sigma points and reports a posterior covariance at every step.

By Minhyeok Ko, Abdollah Shafieezadeh

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arXiv Machine Learning

Aug 13

Unscented KalmanNet: Structure-Preserving Deep Learning with Calibrated Posterior Uncertainty under Incomplete Physics and Unknown Noise

arXiv:2608. 04201v2 Announce Type: replace Abstract: Nonlinear state estimation requires sequentially fusing model-based predictions with noisy measurements.

By Minhyeok Ko, Abdollah Shafieezadeh

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arXiv Machine Learning

Jun 3

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification

arXiv:2606. 02767v1 Announce Type: cross Abstract: Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning.

By Jiho Lee, Nisar R. Ahmed, Rebecca Russell

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Related stories

Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operators

Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter

Deep Spectral Learning of Embedded Latent Transfer Operators for Stochastic Dynamical Systems

Unscented KalmanNet: a hybrid deep learning filter with calibrated posterior covariance for nonlinear state estimation

Unscented KalmanNet: Structure-Preserving Deep Learning with Calibrated Posterior Uncertainty under Incomplete Physics and Unknown Noise

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification