Benchmarking EMlog Calibration for Autonomous Surface Vehicles

2026-09-30

AI Summary

This study addresses the velocity measurement inaccuracies of electromagnetic (EM) logs caused by systematic errors under dynamic sea conditions, proposing a model-based real-time online calibration method. For the first time, an EM log calibration benchmark under dynamic sea states is established. Four calibration models and two estimation pipelines are compared and validated using Kalman filtering alongside real-world telemetry data, demonstrating that maneuvering trajectories offer superior calibration performance over straight-line paths. The results indicate that combining a bias and scale error model with Kalman filtering improves velocity estimation accuracy by 71%, effectively resolving the challenge of precise speed measurement in complex marine environments.

Abstract

Accurate velocity measurement is a fundamental requirement for autonomous surface and underwater vehicles. Commonly, velocity is provided by a Doppler velocity log (DVL) sensor, yet it becomes unavailable due to operational altitude constraints. In such situations, electromagnetic logs (EMLogs) provide a critically robust alternative for continuous velocity estimation. However, raw EMLog measurements are inherently corrupted by systematic errors, which need to be calibrated prior mission begins. Currently, a benchmarking comparative evaluation of how different calibration models perform under rapidly changing dynamic sea conditions is missing in the literature. To bridge this gap, this paper presents a comparative model-based calibration methodology that evaluates four distinct calibration models using two different estimation pipelines. The proposed framework is rigorously validated on a unique 221 minutes of continuous real-world telemetry collected from the MARVEL surface vehicle during dynamic sea trials. The dataset contains two different EMLogs and DVL recordings. Experimental results demonstrate that the bias and scale error model implemented with the Kalman filter improves the speed estimation by 71%. We also demonstrate that dynamical manoeuvres further improve the accuracy compared to standard straight-line paths, ultimately delivering a validated, real-time online calibration EMLog approach for autonomous surface vehicles.

Problem

• Electromagnetic log calibration
• Autonomous surface vehicles
• Velocity estimation
• Benchmarking
• Systematic errors

Innovation

• Electromagnetic Log Calibration
• Kalman Filter
• Autonomous Surface Vehicles
• Velocity Estimation
• Dynamic Manoeuvres