Information-Aided DVL Calibration

2026-06-30

AI Summary

This study addresses the challenge of calibrating Doppler Velocity Logs (DVLs) in GNSS-denied underwater environments, where conventional GNSS-dependent calibration methods fail, leading to degraded navigation accuracy. To overcome this limitation, the authors propose an information-augmented extended Kalman filter framework that integrates external aiding sources to jointly estimate the DVL scale factor and mounting misalignment. This approach enables, for the first time, autonomous DVL self-calibration without GNSS, while further enhancing calibration accuracy when GNSS is available. Experimental validation on real-world autonomous underwater vehicle (AUV) datasets demonstrates an average 20% improvement in calibration accuracy under GNSS coverage and up to a 35% gain in velocity vector estimation precision in GNSS-denied conditions, significantly improving the robustness and reliability of underwater navigation systems.

Abstract

The Doppler velocity log (DVL) velocity measurements are critical to the accuracy of autonomous underwater vehicle (AUV) navigation solutions and, consequently, to mission success. To ensure accurate measurements, the DVL is commonly calibrated before mission start while the AUV sails on the water surface, receiving global navigation satellite system (GNSS) signals that provide accurate reference measurements. Conventionally, Kalman filter-based approaches are employed during calibration to estimate the scale factor and misalignment errors. However, in certain environments, GNSS signals may be unavailable, rendering conventional calibration impossible and forcing the use of uncalibrated DVL measurements, which degrades navigation performance. To address this limitation, this work proposes information-aided calibration (IAC) with two main contributions: first, improving the accuracy of conventional Kalman filter-based calibration in GNSS-enabled environments, and second, enabling GNSS-free DVL self-calibration. Using real-world AUV datasets, the proposed IAC models achieve up to a 20% average improvement in GNSS-enabled environments and up to a 35% improvement in velocity vector estimation during GNSS-free DVL self-calibration. Overall, the proposed approach improves navigation accuracy, reduces navigation drift, and consequently enhances mission reliability.

Problem

• Doppler velocity log
• GNSS-free calibration
• autonomous underwater vehicle
• navigation accuracy
• sensor calibration

Innovation

• Doppler Velocity Log (DVL)
• GNSS-free calibration
• Information-Aided Calibration
• Kalman filter
• Autonomous Underwater Vehicle (AUV)