基于到达时间差测量的水下目标跟踪的移动时域估计
Moving Horizon Estimation for Underwater Target Tracking Based on Time-Difference-of-Arrival Measurements
AI总结：
本文针对水下目标跟踪难题，研究基于到达时间差的移动时域估计方法，仿真显示其鲁棒性优于经典扩展卡尔曼滤波，可作为多智能体跟踪系统的实用组件。
AI中文摘要：
目前，开发用于基于稀疏声学数据定位和跟踪水下人造或自然目标的机器人系统已成为研究热点，例如水面跟踪系统可辅助执行环境监测任务的水下航行器组导航，或研究大型水下动物群的运动模式。当前技术下，后者仅能通过到达时间差（Time-Difference-of-Arrival，TDoA）技术实现。非线性状态估计的最新进展表明，基于优化的方法或可克服经典递归滤波的局限性，但在非线性目标动力学和稀疏测量场景下实现可靠的估计器性能仍是关键挑战。本文研究基于TDoA的水下目标跟踪的移动时域估计（Moving Horizon Estimation，MHE）方法，在捕获典型海洋环境的二维仿真环境中验证，即使经典扩展卡尔曼滤波（Extended Kalman Filter，EKF）变得不可靠，基于MHE的估计器在考虑的场景中仍能保持可靠跟踪。结果表明，MHE方法的关键优势——多步轨迹耦合和物理一致性约束，可显著提升估计器鲁棒性，该方法有望成为未来基于TDoA测量的水下多智能体跟踪系统的实用且可扩展的核心组件。
英文摘要：
There has been a flurry of activity in the development of robotic systems to localize and track underwater man-made or natural targets based on sparse acoustic data. Compelling examples include the development of surface tracking systems to aid in the navigation of groups of underwater vehicles performing environmental monitoring missions or to study the motion patterns of large underwater fauna. With current technology, the latter case can only be tackled using Time-Difference-of-Arrival (TDoA) techniques. Recent progress in nonlinear state estimation indicates that optimization-based methods may overcome the limitations of classical recursive filtering. However, achieving reliable estimator performance in the case of nonlinear target dynamics and sparse measurements remains a key challenge. In this paper, we study a Moving Horizon Estimation (MHE) approach to TDoA-based underwater target tracking. Through a 2D simulation environment capturing typical marine conditions, we show that the MHE-based estimator maintains reliable tracking in the considered scenarios even when the classical EKF becomes unreliable. The results highlight that multi-step trajectory coupling and physically consistent constraints, which are key advantages of the MHE approach, significantly enhance estimator robustness. It is shown that the MHE approach offers promise as a practical and scalable building block for future multi-agent tracking systems based on TDoA measurements operating in real underwater missions.
