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.

翻译：在水下导航系统中，基于捷联惯性导航系统/多普勒计程仪（SINS/DVL）的松耦合架构被广泛采用。传统方法利用SINS解算的姿态将DVL速度从载体坐标系投影到导航坐标系；然而，累积的姿态估计误差会引入速度投影偏差，并在长期运行中降低导航性能。为解决此问题，本文引入了两项互补的改进。首先，通过将姿态误差项纳入观测方程，构建了一种载体姿态误差感知的DVL速度变换模型，以减少投影引起的速度偏差。其次，开发了一种基于协方差矩阵的方差传播方法，用于跨坐标系转换DVL测量不确定性，并引入一个基于期望的姿态误差补偿项，以实现统计一致的噪声建模。仿真和现场实验结果表明，两项改进均能单独提升导航精度，并证实累积的姿态误差同时影响投影后的速度测量值及其相关的不确定性。当联合应用时，长期误差发散得到了有效抑制。现场实验结果表明，与基准IMU+DVL方法相比，所提方法在三维位置均方根误差上实现了78.3%的改进，在最大分量位置误差上减少了71.8%，为提升长期SINS/DVL导航性能提供了一种鲁棒的解决方案。