基于可部署情境触发协方差调度的水下航位推算
Underwater Dead Reckoning with Deployable Situation-Triggered Covariance Scheduling
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- U.S. Army Engineer Research and Development Center, Construction Engineering Research Laboratory（美国陆军工程师研究与发展中心建筑工程研究实验室）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究水下航位推算问题，提出情境触发校准自适应鲁棒扩展卡尔曼滤波器，利用机载概率触发器识别运动情境，通过离线校准和验证集选择调度策略，改进了无视觉航位推算，降低误差且方位误差不变。
AI中文摘要：
水下航位推算在视觉不可用且无法进行外部定位时估计车辆位置。单一的滤波器参数集在许多情况下能良好工作，但在转弯、运动过渡或传感器测量不太可靠的时期，固定调谐可能匹配不佳。本文提出了用于BlueROV2的情境触发校准自适应鲁棒扩展卡尔曼滤波器。机载概率触发器识别当前运动情境，同时一个误差状态滤波器持续运行。当触发器确定时，滤波器仅更改相应的预校准过程和测量噪声矩阵，状态估计、协方差历史、动力学和测量模型不重置或替换。触发器、噪声配置文件和一次性多普勒速度日志偏航对准校正使用稀疏AprilTag监督池运行进行离线校准。一个单独的验证集选择调度策略，然后在留出测试前固定。在四次留出池运行中，该方法相对于具有一个全局噪声配置文件的相同滤波器主干，将每次运行的标签加权平均平移均方根误差从0.488米降低到0.471米，每次留出运行都有利于调度方法。在10秒段上进行配对自举，候选减去基线差异为-0.017米，95%置信区间为[-0.024, -0.008]米，而方位误差基本保持不变。这些结果表明，情境感知协方差调度在不切换估计器或重置滤波器的情况下，提供了适度但一致的无视觉航位推算改进。
英文摘要：
Underwater dead reckoning estimates vehicle position when vision is unavailable and external positioning cannot be assumed. A single set of filter parameters can work well in many situations, but fixed tuning may be poorly matched during turns, motion transitions, or periods when sensor measurements are less reliable. This paper presents the Situation-Triggered Calibrated Adaptive Robust Extended Kalman Filter for a BlueROV2. An onboard probabilistic trigger identifies the current motion situation while one error-state filter runs continuously. When the trigger is confident, the filter changes only to the corresponding pre-calibrated process- and measurement-noise matrices; the state estimate, covariance history, dynamics, and measurement models are not reset or replaced. The trigger, noise profiles, and a one-time Doppler velocity log yaw-alignment correction are calibrated offline using sparse AprilTag-supervised pool runs. A separate validation set selects the scheduling policy, which is then fixed before held-out testing. Across four held-out pool runs, the method reduces label-weighted mean per-run translation root-mean-square error from 0.488 m to 0.471 m relative to the same filter backbone with one global noise profile, and every held-out run favors the scheduled method. A paired bootstrap over 10-second segments gives a candidate-minus-baseline difference of -0.017 m with a 95% confidence interval of [-0.024, -0.008] m, while orientation error remains essentially unchanged. These results indicate that situation-aware covariance scheduling provides a modest but consistent vision-free dead-reckoning improvement without switching estimators or resetting the filter.
