A DVL Aided Loosely Coupled Inertial Navigation Strategy for AUVs with Attitude Error Modeling and Variance Propagation

2026-01-27

AI Summary

This work addresses the degradation of long-term navigation accuracy in loosely coupled SINS/DVL systems, where accumulating attitude errors induce biases in DVL velocity projection. To mitigate this issue, the authors propose a novel attitude error-aware DVL velocity transformation model that dynamically compensates for projection errors by integrating cross-frame statistically consistent noise modeling with covariance propagation. While preserving the computational simplicity of the loose coupling architecture, the method significantly suppresses long-term error divergence. Experimental results demonstrate a 78.3% reduction in 3D position RMSE and a 71.8% decrease in maximum component-wise position error compared to the baseline approach, thereby substantially enhancing the long-term navigation accuracy of underwater vehicles.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

In underwater navigation systems, strap-down inertial navigation system/Doppler velocity log (SINS/DVL)-based loosely coupled architectures are widely adopted. Conventional approaches project DVL velocities from the body coordinate system to the navigation coordinate system using SINS-derived attitude; however, accumulated attitude estimation errors introduce biases into velocity projection and degrade navigation performance during long-term operation. To address this issue, two complementary improvements are introduced. First, a vehicle attitude error-aware DVL velocity transformation model is formulated by incorporating attitude error terms into the observation equation to reduce projection-induced velocity bias. Second, a covariance matrix-based variance propagation method is developed to transform DVL measurement uncertainty across coordinate systems, introducing an expectation-based attitude error compensation term to achieve statistically consistent noise modeling. Simulation and field experiment results demonstrate that both improvements individually enhance navigation accuracy and confirm that accumulated attitude errors affect both projected velocity measurements and their associated uncertainty. When jointly applied, long-term error divergence is effectively suppressed. Field experimental results show that the proposed approach achieves a 78.3% improvement in 3D position RMSE and a 71.8% reduction in the maximum component-wise position error compared with the baseline IMU+DVL method, providing a robust solution for improving long-term SINS/DVL navigation performance.

Problem

• SINS/DVL
• attitude error
• velocity projection bias
• underwater navigation
• long-term error divergence

Innovation

• attitude error modeling
• variance propagation
• DVL-aided navigation
• loosely coupled INS
• underwater navigation