As the rapid development of aviation industry and newly emerging crowd-sourcing projects such as Flightradar24 and FlightAware,large amount of air traffic data,particularly four-dimension(4D)trajectory data,have becom...As the rapid development of aviation industry and newly emerging crowd-sourcing projects such as Flightradar24 and FlightAware,large amount of air traffic data,particularly four-dimension(4D)trajectory data,have become available for the public.In order to guarantee the accuracy and reliability of results,data cleansing is the first step in analyzing 4D trajectory data,including error identification and mitigation.Data cleansing techniques for the 4D trajectory data are investigated.Back propagation(BP)neural network algorithm is applied to repair errors.Newton interpolation method is used to obtain even-spaced trajectory samples over a uniform distribution of each flight’s 4D trajectory data.Furthermore,a new method is proposed to compress data while maintaining the intrinsic characteristics of the trajectories.Density-based spatial clustering of applications with noise(DBSCAN)is applied to identify remaining outliers of sample points.Experiments are performed on a data set of one-day 4D trajectory data over Europe.The results show that the proposed method can achieve more efficient and effective results than the existing approaches.The work contributes to the first step of data preprocessing and lays foundation for further downstream 4D trajectory analysis.展开更多
基金supported by the National Natural Science Foundations of China (Nos. 61861136005,61851110763,and 71731001).
文摘As the rapid development of aviation industry and newly emerging crowd-sourcing projects such as Flightradar24 and FlightAware,large amount of air traffic data,particularly four-dimension(4D)trajectory data,have become available for the public.In order to guarantee the accuracy and reliability of results,data cleansing is the first step in analyzing 4D trajectory data,including error identification and mitigation.Data cleansing techniques for the 4D trajectory data are investigated.Back propagation(BP)neural network algorithm is applied to repair errors.Newton interpolation method is used to obtain even-spaced trajectory samples over a uniform distribution of each flight’s 4D trajectory data.Furthermore,a new method is proposed to compress data while maintaining the intrinsic characteristics of the trajectories.Density-based spatial clustering of applications with noise(DBSCAN)is applied to identify remaining outliers of sample points.Experiments are performed on a data set of one-day 4D trajectory data over Europe.The results show that the proposed method can achieve more efficient and effective results than the existing approaches.The work contributes to the first step of data preprocessing and lays foundation for further downstream 4D trajectory analysis.
文摘针对含有噪声和外点的三维点云刚体配准问题,由于迭代最近点(iterative closest point,ICP)算法的配准精度较低,为此,该文提出了一种基于改进ICP算法的三维点云刚体配准方法。考虑到伪Huber损失函数对噪声和外点不敏感、鲁棒性强,首先,建立了基于伪Huber损失函数的三维点云刚体配准模型。其次,利用RGB-D点云数据中颜色信息辅助建立点云对应关系,以提高改进ICP算法中对应点匹配的准确性。最后,结合奇异值分解(singular value decomposition,SVD)和Levenberg-Marquardt(LM)的优化算法对三维点云刚体配准模型进行优化求解。实验结果表明,该文所提三维点云刚体配准方法的配准精度高,能够有效抑制噪声和外点对配准精度的影响。
