The paper introduces COIN-GP, an observer‑based cooperative learning framework for distributed sensor networks that jointly estimates system states and partially unknown dynamics using online distributed Gaussian Process regression. It addresses challenges of incomplete state observations and deficient GP models by proposing a novel data collection strategy with theoretical feasibility conditions. The authors also derive an error upper bound for both state and model estimation and demonstrate through simulations that COIN‑GP outperforms existing distributed GP‑based methods.
By Zewen Yang, Xiaobing Dai, Zhenxiao Yin, Hang Zhao, Zhijun Li, C. C. Chan
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
The paper introduces COIN-GP, an observer‑based dynamic cooperative learning framework for distributed sensor networks that jointly estimates system states and partially unknown dynamics when only partial state observations are available. It employs online distributed Gaussian Process regression and a novel data‑collection strategy with theoretical feasibility conditions, and derives an error upper bound that combines state and model estimation errors. Simulations show that COIN‑GP outperforms existing distributed GP‑based methods.
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
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: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: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
The paper introduces AURA, a meta‑learning framework that learns a low‑dimensional latent state‑space model for the evolution of optimal model parameters under distribution shift. Online adaptation is performed via extended Kalman filtering in this latent space, followed by reconstruction of full model parameters through a learned lifting map, enabling efficient single‑step updates. Experiments on neural wireless receivers and non‑stationary image classification show that AURA improves adaptation speed, accuracy, and computational efficiency compared to existing online learning and Bayesian filtering baselines.
By Guy Gerson, Tomer Raviv, Nir Shlezinger, Tirza Routtenberg, Osvaldo Simeone
arXiv:2608. 04201v2 Announce Type: replace Abstract: Nonlinear state estimation requires sequentially fusing model-based predictions with noisy measurements.
By Minhyeok Ko, Abdollah Shafieezadeh
FILT3R is a training‑free latent filtering layer for streaming 3D reconstruction that treats recurrent state updates as stochastic state estimation in token space. It maintains per‑token variance and computes a Kalman‑style gain to balance memory retention with new observations, estimating process noise online from temporal drift of candidate tokens. Experiments show that FILT3R generalizes overwrite and gating policies, shrinking gains in stable regimes and increasing them during genuine scene changes, thereby improving long‑horizon stability for depth, pose, and 3D reconstruction.
By Seonghyun Jin, Jong Chul Ye
arXiv:2607. 12975v1 Announce Type: cross Abstract: Data assimilation estimates the state of a dynamical system from model forecasts and incoming observations.
By Zhuoyuan Li, Yue Zhao, Ming Li
The paper introduces two distributed recursive Gaussian process algorithms, ADMM‑RGP and PDMM‑RGP, designed for multi‑output regression in multi‑agent systems. It analyzes their stability and convergence, proposes parameter selection strategies to speed up convergence, and demonstrates that the methods reduce communication overhead while preserving estimation accuracy and consensus. Experiments on a real‑world wind dataset confirm the algorithms’ effectiveness across different communication graph connectivities.
By Josephine King, Ali Emre Balci, Raj Thilak Rajan