7 papers · 1 filter
DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation
Arup Kumar Sahoo, Itzik Klein
Autonomous Underwater Vehicles (AUVs) rely heavily on the fusion of inertial sensors and Doppler velocity logs (DVLs) for navigation. In standard autonomous navigation systems, the…
BLENDS: Bayesian Learning-Enhanced Deep Smoothing for GNSS-Denied Environments
Nadav Cohen, Itzik Klein
Maintaining accurate navigation during GNSS outages remains a significant challenge for autonomous systems relying on low-cost inertial sensors. While classical smoothing methods,…
Wheel-Mounted/GNSS Fusion with AI-Aided Position Updates
Gal Versano, Itzik Klein
Accurate and robust localization remains a fundamental challenge for autonomous ground vehicles. In this work, we propose a hybrid neural inertial navigation framework that integra…
Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements
Gal Versano, Itzik Klein
Accurate and reliable navigation is essential for autonomous ground vehicle operations. Standard INS/GNSS fusion relies on GNSS position updates, which provide limited observabilit…
A Hybrid Neural-Assisted Unscented Kalman Filter for Unmanned Ground Vehicle Navigation
Gal Versano, Itzik Klein
Modern autonomous navigation for unmanned ground vehicles relies on different estimators to fuse inertial sensors and GNSS measurements. However, the constant noise covariance matr…
Unscented Kalman Filter with a Nonlinear Propagation Model for Navigation Applications
Amit Levy, Itzik Klein
The unscented Kalman filter is a nonlinear estimation algorithm commonly used in navigation applications. The prediction of the mean and covariance matrix is crucial to the stable…