arXiv AI June 30, 2026 By George Stamatelis, Kyriakos Stylianopoulos, George C. Alexandropoulos

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

agents

arXiv AI

Jun 2

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.

By Adnan Harun Dogan, Berken Utku Demirel, Christian Holz

computer-vision robotics benchmarks

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

safety

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 12

Two-Layer Linear Auto-Regressive Models Estimate Latent States

arXiv:2606. 12691v1 Announce Type: cross Abstract: Auto-regressive models have emerged as powerful tools for sequential data, from language to video.

By Yahya Sattar, Sunmook Choi, Leo Maynard-Zhang, Yassir Jedra, Maryam Fazel, Sarah Dean

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

arXiv Machine Learning

6d ago

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

safety

Related stories

FW-NKF: Frequency-Weighted Neural Kalman Filters

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

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

Two-Layer Linear Auto-Regressive Models Estimate Latent States

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

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