Unscented Kalman Filtering

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Unscented KalmanNet: Structure-Preserving Deep Learning with Calibrated Posterior Uncertainty under Incomplete Physics and Unknown Noise
Aug 4, 2026Minhyeok Ko, Abdollah ShafieezadehKalman Filtering Unscented Kalman Filtering
Department of Civil and Construction Engineering and Management, The University of Texas at Tyler, Tyler, TX, 75799, USA · Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, OH, 43210, USA
Wind and State Estimation on SE(3): Comparative Evaluation of EKF and UKF with Continuous and Discrete Quadrotor Models
Jun 29, 2026Hiranya Udagedara, Adam Bigsby, Mahdis BishebanAerial Robotics Kalman Filtering
Dept. of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Alberta, Canada
Realtime Wind Estimation using Low Cost Quadrotor Uncrewed Aerial Vehicles
Jun 29, 2026Hiranya Udagedara, Mahdis BishebanKalman Filtering Unscented Kalman Filtering
Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, Canada
SUMO: Segment and Track Any Motion with Nonlinear State Space Models
Jun 29, 2026Kexin Tian, Sixu Li, Keshu Wu +2Video Object Segmentation Visual Object Tracking
Texas A&M University
Dual Quaternion-Based Unscented Kalman Filter with Visual Inertial Odometry for Navigation in GPS-Denied Environments
Jun 8, 2026Mohamed Khalifa, Hashim A. HashimKalman Filtering Inertial Odometry
Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, Ontario, K1S-5B6, Canada
Uncertainty-Aware Adaptive Sensor Fusion for Autonomous Navigation
Jun 3, 2026Simegnew Yihunie Alaba, Yuichi MotaiUnscented Kalman Filtering Visual-Inertial Odometry
Department of Electrical and Computer Engineering, College of Engineering, Virginia Commonwealth University, Richmond, VA 23220, USA
Fixed-Time Dynamic Landing of Quadrotors using Adaptive Unscented Kalman Filtering and Nonlinear Model Predictive Control
Jun 1, 2026Mohammadreza Izadi, Zeinab Shayan, Steven Waslander +1Kalman Filtering Nonlinear MPC
Autonomous Vehicles Laboratory, Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, Canada · University of Toronto Institute for Aerospace Studies, University of Toronto, Toronto, Canada
FusionCore: A 23-State Unscented Kalman Filter for IMU, Wheel Encoder, GPS, and Visual SLAM Fusion in ROS 2
May 24, 2026Manan KharwarKalman Filtering Unscented Kalman Filtering
Independent Researcher, Hamilton, ON, Canada
Weighted Flow Matching and Physics-Informed Nonlinear Filtering for Parameter Estimation in Digital Twins
May 16, 2026Yasar Yanik, Himadri Basu, Ricardo G. Sanfelice +1Flow Matching Kalman Filtering
Department of Applied Mathematics, University of California, Santa Cruz, CA 95064, USA · Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA 95064, USA
Observability Conditions and Filter Design for Visual Pose Estimation via Dual Quaternions
May 3, 2026Nicholas B. Andrews, Kristi A. MorgansenDynamical Systems Kalman Filtering
Department of Aeronautics and Astronautics, University of Washington, Seattle, WA 98195 USA

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

Minhyeok Ko, Abdollah Shafieezadeh

Department of Civil and Construction Engineering and Management, The University of Texas at Tyler, Tyler, TX, 75799, USA · Department of Civil, Environmental, and Geodetic Engineering, The Ohio State University, Columbus, OH, 43210, USA

Kalman Filtering Unscented Kalman Filtering Covariance Estimation Robot State Estimation Uncertainty Calibration

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Nonlinear state estimation requires sequentially fusing model-based predictions with noisy measurements. Under imperfect dynamics and unknown, time-varying noise statistics, this fusion can degrade in both accuracy and statistical consistency. Existing learning-aided filters largely treat accuracy and uncertainty estimation separately, limiting their ability to correct model-mismatch-induced bias while retaining an explicit, calibrated posterior covariance. This paper introduces Unscented KalmanNet (UKN), a model-based deep learning architecture that extends the Unscented Kalman Filter (UKF) with learned mechanisms for these two sources of filtering error while preserving explicit posterior covariance propagation. NoiseNet learns time-varying process and measurement covariances as bounded multiplicative corrections to baseline covariances, guaranteeing positive definiteness, while GainNet learns a bounded residual correction to the analytical UKF gain to compensate for model-mismatch-induced bias. A calibration-aware training objective couples state error with posterior covariance and innovation consistency terms through adaptive weighting, jointly optimizing accuracy and calibration. UKN is benchmarked against UKF, KalmanNet, and Bayesian KalmanNet on three synthetic systems and real-flight UZH-FPV data. It achieves the lowest state-estimation error in all four examples and reduces RMSE by 26.4-49.7% compared with UKF in the synthetic cases. Leave-one-sequence-out cross-validation over 11 flights shows 22.4% and 34.3% reductions in mean position and velocity RMSE, respectively. UKN also yields the lowest fold-to-fold variability, with dimension-normalized NEES and empirical coverage closest to nominal values among covariance-reporting filters. These results show that structured learned adaptation improves estimation accuracy while retaining calibrated uncertainty.

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