1 citations · 2 across the 10 of their papers we have counts for
11 papers
Benchmarking EMlog Calibration for Autonomous Surface Vehicles
Samuel Cohen-Salmon, Itzik Klein
Accurate velocity measurement is a fundamental requirement for autonomous surface and underwater vehicles. Commonly, velocity is provided by a Doppler velocity log (DVL) sensor, ye…
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…
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…
Neural Aided Adaptive Innovation-Based Invariant Kalman Filter
Barak Diker, Itzik Klein
Autonomous platforms require accurate positioning to complete their tasks. To this end, a Kalman filter-based algorithms, such as the extended Kalman filter or invariant Kalman fil…
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…
Design and Experimental Validation of an Autonomous USV for Sensor Fusion-Based Navigation in GNSS-Denied Environments
Samuel Cohen-Salmon, Itzik Klein
This paper presents the design, development, and experimental validation of MARVEL, an autonomous unmanned surface vehicle built for real-world testing of sensor fusion-based navig…