A Physics-Based Method for Drifting Buoy Trajectory Backtracking with Uncertainty Quantification

LI Hui; WANG Shui; XIONG Xinquan; YUE Peng; MO Lihong; WAN Wanghua

doi:10.11993/j.issn.2096-3920.2026-0014

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LI Hui, WANG Shui, XIONG Xinquan, YUE Peng, MO Lihong, WAN Wanghua. A Physics-Based Method for Drifting Buoy Trajectory Backtracking with Uncertainty Quantification[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0014

Citation:

LI Hui, WANG Shui, XIONG Xinquan, YUE Peng, MO Lihong, WAN Wanghua. A Physics-Based Method for Drifting Buoy Trajectory Backtracking with Uncertainty Quantification[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0014

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A Physics-Based Method for Drifting Buoy Trajectory Backtracking with Uncertainty Quantification

doi: 10.11993/j.issn.2096-3920.2026-0014

college of Intelligent system science and engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China

Received Date: 2026-01-14
Accepted Date: 2026-02-09
Rev Recd Date: 2026-02-02

Available Online: 2026-07-08

Abstract

Abstract

High-precision trajectory backtracking technology for drifting buoys is urgently needed for maritime search and rescue (SAR) and pollution source tracing, yet traditional Lagrangian models exhibit significant errors in complex marine environments. This study proposes a physics-driven trajectory backtracking model for marine drifting buoys, which innovatively introduces a dynamic diffusion coefficient based on autocorrelation analysis of wind and current field time series to optimize the subgrid-scale velocity compensation mechanism in random walk models. The model integrates a drift dynamics framework incorporating wind forcing, ocean currents, and Coriolis force, and combines Monte Carlo simulation with kernel density estimation to quantify the spatiotemporal uncertainties in trajectory backtracking. Validated using North Atlantic Ocean Internet of Things buoy data across four typical marine environments—tropical open ocean, current convergence zones, temperate westerlies, and nearshore complex terrain—the proposed model achieves 72-hour trajectory backtracking errors of 3.9-5.8 km. Compared with traditional Lagrangian models, accuracy improvements of 74%, 55%, 59%, and 22% are achieved in the four sea areas respectively, effectively addressing the trajectory backtracking accuracy problem in complex marine environments and providing reliable technical support for maritime emergency rescue and pollution source localization.
- drifting buoy,
- trajectory backtracking,
- dynamic diffusion coefficient,
- kernel density estimation

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References(30)

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