10 papers
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,…
Gauge Freedom Optimization for Truncation Error Reduction in Inertial Navigation
Yaakov Libero, Itzik Klein
Numerical integration plays a central role in inertial navigation systems, where sensor measurements are propagated through time to obtain orientation, velocity, and position state…
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…
Multi-Scaled Unscented Kalman Filter
Amit Levy, Itzik Klein
The unscented Kalman filter (UKF) is a commonly used algorithm capable of estimating the states of nonlinear dynamic systems. It carefully chooses a set of sample points, called si…