用声学飞行时间实现低成本微浮标的高精度水下定位
Robust Underwater Localization of Buoyancy Driven microFloats Using Acoustic Time-of-Flight Measurements
- 双向声学测时,结合几何代价与克拉美罗界过滤异常值
- 中位定位误差低于4米,均值误差从139.29米降至12.07米
- 适合需要高频、鲁棒水下定位的海洋观测项目
精确水下定位对需高频位置更新的低成本自主平台仍是挑战。本文提出一种针对近岸水域运行的浮力驱动微浮标的鲁棒、低成本定位流程。基于前人工作,引入双向声学飞行时间(ToF)定位框架,包含浮标到浮标和浮标到浮标传输,增加可用测量数。方法融合非线性三边定位与基于几何代价和克拉美罗下界(CRLB)的位置估计过滤,剔除多径效应等声学误差导致的异常值,提升定位鲁棒性,无需依赖重型平滑。在华盛顿州普吉特湾两次实地部署中验证,定位流程相对于GPS位置的中位误差低于4米。过滤技术使均值误差从139.29米降至12.07米,轨迹与GPS路径对齐更优。此外,我们演示了对未回收浮标在实验期间持续发射信号的到达时间差(TDoA)定位。基于距离的声学定位技术应用广泛且硬件无关;本工作通过精细算法设计,提升其定位频率与鲁棒性。
原文摘要 · Abstract (English)
Accurate underwater localization remains a challenge for inexpensive autonomous platforms that require highfrequency position updates. In this paper, we present a robust, low-cost localization pipeline for buoyancy-driven microFloats operating in coastal waters. We build upon previous work by introducing a bidirectional acoustic Time-of-Flight (ToF) localization framework, which incorporates both float-to-buoy and buoy-to-float transmissions, thereby increasing the number of usable measurements. The method integrates nonlinear trilateration with a filtering of computed position estimates based on geometric cost and Cramer-Rao Lower Bounds (CRLB). This approach removes outliers caused by multipath effects and other acoustic errors from the ToF estimation and improves localization robustness without relying on heavy smoothing. We validate the framework in two field deployments in Puget Sound, Washington, USA. The localization pipeline achieves median positioning errors below 4 m relative to GPS positions. The filtering technique shows a reduction in mean error from 139.29 m to 12.07 m, and improved alignment of trajectories with GPS paths. Additionally, we demonstrate a Time-Difference-of-Arrival (TDoA) localization for unrecovered floats that were transmitting during the experiment. Range-based acoustic localization techniques are widely used and generally agnostic to hardware-this work aims to maximize their utility by improving positioning frequency and robustness through careful algorithmic design.
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