Introduction of turbulence field related content.

To address the problem that vertical wind data can provide information only on turbulence fluctuations in the vertical direction, meaning that the consideration of insufficiently compre-hensive turbulence information leads to low accuracy in eddy dissipation rate(EDR) estimation based on vertical wind (VWE), the paper proposes a flight route EDR estimation using MHE to fuse three-dimensional wind information in the Quick Access Recorder(QAR) data analysis. Quality control is performed on QAR data to eliminate irrelevant features for computing vertical wind and the two horizontal wind components. The corresponding formulas are then applied to obtain three-dimensional wind data information. Subsequently, a multi-head attention mechanism is employed to quantify the relationship between three-dimensional wind characteristics and the feature matrix (vertical acceleration). This process generates feature fusion weights, which are mapped onto the three-dimensional wind matrix and used to convert the three-dimensional wind data into one-dimensional wind data, thereby achieving the fusion of three-dimensional wind features. Finally, through maximum likelihood estimation(MLE) combine the new wind data in the frequency domain, the estimation of the flight route EDR is realized. Experimental results confirmed that the proposed estimation algorithm exhibits optimal performance compared to EDR estimations based on PCE and VWE, and the estimated values have smaller errors relative to the true EDR values. The proposed algorithm provides highly accurate EDR estimations for flight paths and demonstrates good practical applicability for assessing turbulence intensity along flight routes, thereby enhancing the safety of aircraft during flight.

针对垂直风数据仅能提供垂直方向湍流脉动信息，导致湍流信息考量不够全面，进而使得基于垂直风的涡耗散率（eddy dissipation rate, EDR）估算精度偏低的问题，本文提出一种借助移动视界估计（Moving Horizon Estimation, MHE）融合快速存取记录器（Quick Access Recorder, QAR）数据分析中的三维风信息的飞行航迹EDR估算方法。首先对QAR数据开展质量控制，剔除与垂直风及两个水平风分量计算无关的冗余特征。随后通过对应公式推导获取三维风数据信息。后续采用多头注意力机制（multi-head attention mechanism）量化三维风特征与特征矩阵（垂直加速度）之间的关联，生成特征融合权重并将其映射至三维风矩阵，以此将三维风数据转换为一维风数据，从而实现三维风特征的融合。最后通过最大似然估计（Maximum Likelihood Estimation, MLE）对频域内的新风数据进行融合，完成飞行航迹EDR的估算。实验结果表明，相较于基于PCE与垂直风的EDR估算方法，本文提出的估算算法性能最优，其估算值与真实EDR值相比误差更小。该算法可为飞行航迹提供高精度的EDR估算，在评估飞行航线上的湍流强度方面展现出良好的实际应用价值，进而提升航空器飞行过程中的安全性。