自主水面车辆EMLog校准基准测试
Benchmarking EMlog Calibration for Autonomous Surface Vehicles
浏览论文内容
中文总结 AI 辅助
本文针对自主水面车辆，提出了一种基于模型的EMLog校准基准测试方法，通过比较四种校准模型和两种估计流程，在221分钟真实海试数据上验证了卡尔曼滤波器结合偏差和尺度误差模型可将速度估计提升71%，并证明动态机动可进一步提高准确性。
中文摘要 AI 辅助
精确的速度测量是自主水面和水下车辆的基本要求。通常，速度由多普勒速度计程仪（DVL）传感器提供，但由于操作高度限制，该传感器可能变得不可用。在这种情况下，电磁计程仪（EMLogs）为连续速度估计提供了一种关键的稳健替代方案。然而，原始的EMLog测量值固有地受到系统误差的影响，需要在任务开始前进行校准。目前，文献中缺乏对不同校准模型在快速变化的动态海况下性能的基准比较评估。为填补这一空白，本文提出了一种基于模型的比较校准方法，使用两种不同的估计流程评估四种不同的校准模型。所提出的框架在MARVEL水面车辆动态海试期间收集的221分钟连续真实遥测数据上进行了严格验证。数据集包含两种不同的EMLog和DVL记录。实验结果表明，采用卡尔曼滤波器实现的偏差和尺度误差模型将速度估计提高了71%。我们还证明，与标准直线路径相比，动态机动进一步提高了准确性，最终为自主水面车辆提供了一种经过验证的实时在线校准EMLog方法。
英文摘要
Accurate velocity measurement is a fundamental requirement for autonomous surface and underwater vehicles. Commonly, velocity is provided by a Doppler velocity log (DVL) sensor, yet it becomes unavailable due to operational altitude constraints. In such situations, electromagnetic logs (EMLogs) provide a critically robust alternative for continuous velocity estimation. However, raw EMLog measurements are inherently corrupted by systematic errors, which need to be calibrated prior mission begins. Currently, a benchmarking comparative evaluation of how different calibration models perform under rapidly changing dynamic sea conditions is missing in the literature. To bridge this gap, this paper presents a comparative model-based calibration methodology that evaluates four distinct calibration models using two different estimation pipelines. The proposed framework is rigorously validated on a unique 221 minutes of continuous real-world telemetry collected from the MARVEL surface vehicle during dynamic sea trials. The dataset contains two different EMLogs and DVL recordings. Experimental results demonstrate that the bias and scale error model implemented with the Kalman filter improves the speed estimation by 71%. We also demonstrate that dynamical manoeuvres further improve the accuracy compared to standard straight-line paths, ultimately delivering a validated, real-time online calibration EMLog approach for autonomous surface vehicles.
发表机构
- University of Haifa（海法大学）
机构由 AI 辅助整理，请以论文原文为准。
