Autonomous landing of Uncrewed Aerial Vehicles (UAVs) on oscillating marine platforms is severely constrained by wave-induced multi-frequency oscillations, wind disturbances, and prediction phase lags in motion prediction. Existing methods either treat platform motion as a general random process or lack explicit modeling of wave spectral characteristics, leading to suboptimal performance under dynamic sea conditions. To address these limitations, we propose SpecFuse: a novel spectral-temporal fusion predictive control framework that integrates frequency-domain wave decomposition with time-domain recursive state estimation for high-precision 6-DoF motion forecasting of Uncrewed Surface Vehicles (USVs). The framework explicitly models dominant wave harmonics to mitigate phase lags, refining predictions in real time via IMU data without relying on complex calibration. Additionally, we design a hierarchical control architecture featuring a sampling-based HPO-RRT* algorithm for dynamic trajectory planning under non-convex constraints and a learning-augmented predictive controller that fuses data-driven disturbance compensation with optimization-based execution. Extensive validations (2,000 simulations + 8 lake experiments) show our approach achieves a 3.2 cm prediction error, 4.46 cm landing deviation, 98.7% / 87.5% success rates (simulation / real-world), and 82 ms latency on embedded hardware, outperforming state-of-the-art methods by 44%-48% in accuracy. Its robustness to wave-wind coupling disturbances supports critical maritime missions such as search and rescue and environmental monitoring. All code, experimental configurations, and datasets will be released as open-source to facilitate reproducibility.

翻译：无人机在振荡海洋平台上的自主着陆任务，受到波浪诱导的多频振荡、风扰动以及运动预测中的相位滞后的严重制约。现有方法要么将平台运动视为一般随机过程，要么缺乏对波浪频谱特性的显式建模，导致在动态海况下性能欠佳。为应对这些局限，我们提出SpecFuse：一种新颖的谱-时融合预测控制框架，该框架将频域波浪分解与时域递归状态估计相结合，用于无人水面艇的高精度六自由度运动预测。该框架显式建模主导波浪谐波以缓解相位滞后，并通过IMU数据实时优化预测，无需依赖复杂校准。此外，我们设计了一种分层控制架构，其特点包括：在非凸约束下进行动态轨迹规划的基于采样的HPO-RRT*算法，以及一个学习增强的预测控制器，该控制器融合了数据驱动的扰动补偿与基于优化的执行。大量验证（2000次仿真 + 8次湖上实验）表明，我们的方法实现了3.2厘米的预测误差、4.46厘米的着陆偏差、98.7% / 87.5%的成功率（仿真/实景），以及在嵌入式硬件上82毫秒的延迟，在精度上优于现有最先进方法44%-48%。其对波浪-风耦合扰动的鲁棒性，支持诸如搜救和环境监测等关键海上任务。所有代码、实验配置和数据集将作为开源发布，以促进可复现性。