用高斯过程提升水下导航速度估计精度，减少漂移误差。
Gaussian Process Regression for Improved Underwater Navigation
- 采用多输出高斯过程回归，直接学习传感器偏差并输出不确定性。
- 实测数据表明速度估计误差降低约20%，姿态状态精度显著提升。
- 适合水下机器人、自主航行器等需要高鲁棒性导航的场景。
由于缺乏全球导航卫星系统信号，水下导航面临巨大挑战，惯性导航系统随时间累积漂移。通常使用多普勒速度计（DVL）通过速度测量来缓解漂移，传统方法如最小二乘法（LS）在理想条件下有效，但未考虑传感器偏差，性能受限。本文提出一种基于多输出高斯过程回归（MOGPR）的数据驱动替代方案，用于改进DVL速度估计。MOGPR不仅能提供速度估计值，还可输出测量协方差，支持在误差状态扩展卡尔曼滤波器（EKF）中实现自适应融合。我们使用真实无人潜水器（AUV）数据对方法进行评估，并与LS和先进深度学习模型BeamsNet对比。结果表明，MOGPR使速度估计误差降低约20%，同时显著提升整体导航精度，尤其在姿态状态上表现更优。此外，引入的不确定性估计实现了自适应EKF框架，在动态水下环境中增强了导航鲁棒性。
原文摘要 · Abstract (English)
Accurate underwater navigation is a challenging task due to the absence of global navigation satellite system signals and the reliance on inertial navigation systems that suffer from drift over time. Doppler velocity logs (DVLs) are typically used to mitigate this drift through velocity measurements, which are commonly estimated using a parameter estimation approach such as least squares (LS). However, LS works under the assumption of ideal conditions and does not account for sensor biases, leading to suboptimal performance. This paper proposes a data-driven alternative based on multi-output Gaussian process regression (MOGPR) to improve DVL velocity estimation. MOGPR provides velocity estimates and associated measurement covariances, enabling an adaptive integration within an error-state Extended Kalman Filter (EKF). We evaluate our proposed approach using real-world AUV data and compare it against LS and a state-of-the-art deep learning model, BeamsNet. Results demonstrate that MOGPR reduces velocity estimation errors by approximately 20% while simultaneously enhancing overall navigation accuracy, particularly in the orientation states. Additionally, the incorporation of uncertainty estimates from MOGPR enables an adaptive EKF framework, improving navigation robustness in dynamic underwater environments.
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