提出新方法提升惯导与多普勒计速仪融合定位精度，尤其在姿态不确定时更稳定。
Analytical Covariance Propagation for DVL-Aided Loosely Coupled SINS Under Attitude Uncertainty
- 基于姿态误差建模，精确传播速度观测和协方差，避免传统近似
- 实测数据中定位误差比现有方法降低超48%，垂直方向降幅达74.8%
- 适用于需高精度姿态依赖测量融合的场景，如水下导航系统
在松耦合捷联惯性导航系统/多普勒速度计（SINS/DVL）融合中，体坐标系下的DVL速度需通过SINS计算的姿态投影至导航坐标系。姿态不确定性会同时影响投影后的速度观测及其测量协方差。本文提出一种解析协方差传播（ACP）方法，一致处理这两类影响：姿态误差感知的观测模型刻画投影后速度的扰动，协方差构造在矩阵层面传播体坐标系下的DVL协方差，并加入由预测姿态误差协方差推导出的闭式项，而非直接旋转分量标准差。仿真与受控水面实地实验对比了ACP与传统SINS/DVL及自适应协方差基准方法。在所评估的数据集与参数设置下，ACP在位置误差指标上表现最优。相对于变分贝叶斯自适应卡尔曼滤波器（VBAKF），ACP在实地实验中使北向、东向和垂向位置均方根误差（RMSE）分别降低48.5%、55.0%和74.8%。该方法不仅适用于DVL辅助，还为姿态依赖向量测量融合提供可迁移的协方差构建原则，是启发式坐标系相关协方差设定的物理可解释替代方案。
原文摘要 · Abstract (English)
In loosely coupled strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integration, the body-frame DVL velocity is projected into the navigation frame using the SINS-computed attitude. Attitude uncertainty consequently affects both the projected velocity observation and its associated measurement covariance. This paper develops an analytical covariance propagation (ACP) method that treats these two effects consistently. The attitude-error-aware observation model represents perturbations in the projected DVL velocity, whereas the covariance construction propagates the body-frame DVL covariance at the matrix level and adds a closed-form term derived from the predicted attitude-error covariance, rather than directly rotating component-wise standard deviations. Simulation and a controlled surface-water field experiment compare ACP with conventional SINS/DVL and adaptive-covariance benchmarks. For the evaluated data sets and parameterizations, ACP yields the lowest position-error metrics among the compared methods. Relative to the variational Bayesian adaptive Kalman filter (VBAKF) in the field experiment, ACP reduces the northward, eastward, and downward position root-mean-square errors (RMSEs) by 48.5%, 55.0%, and 74.8%, respectively. Beyond DVL aiding, ACP provides a transferable covariance-construction principle for attitude-dependent vector-measurement fusion and a physically interpretable alternative to heuristic frame-dependent covariance assignment.
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