Parameter-independent aircraft flight path clustering method based on contour coefficients

Abstract

The invention discloses a parameter-independent aircraft flight path clustering method based on contour coefficients. The parameter-independent aircraft flight path clustering method comprises the following steps: firstly, normalizing spatial position coordinates of all tracks in a track set; then, establishing a track similarity, a track distance matrix, a degree matrix and a Laplace matrix basedon the dynamic time bending distance and a Gaussian kernel function; next, clustering the track feature subspaces cluster by cluster in a given interval by utilizing a k-means algorithm; and finally,determining the optimal cluster, the optimal cluster number and the maximum average contour coefficient by taking the average contour coefficient of the track set as an evaluation standard of clustering quality. Compared with an existing method, the parameter-independent aircraft flight path clustering method has the advantages that expert experience or domain knowledge is not needed; human intervention is eliminated; the objectivity of the clustering process and result is high; and the parameter-independent aircraft flight path clustering method is not influenced by flight path length, speed, horizontal and vertical coordinates and height coordinate value domain difference, and is suitable for various data formats; the workload of user parameter adjustment is reduced, and the time cost is saved.

Description

technical field [0001] The invention relates to a time-space trajectory clustering method, in particular to a parameter-independent aircraft flight trajectory clustering method based on silhouette coefficients. Background technique [0002] Most of the current flight trajectory clustering algorithms require one or more input parameters, and their clustering results usually depend heavily on these parameters, but it is difficult to determine reasonable parameters. Generally, there are three methods and basis to determine the necessary parameters of the algorithm. The first method relies on expert experience or domain knowledge. The disadvantage is that it relies on expert experience, is highly subjective, and lacks objective quantitative evaluation indicators. The second method relies on the data characteristics of trajectory samples. The disadvantages are strong limitations and poor generalization ability. When the characteristics of trajectory test samples and training sam...

AI Technical Summary

Problems solved by technology

The first method relies on expert experience or domain knowledge. The disadvantage is that it relies on expert experience, is highly subjective, and lacks objective quantitative evaluation indicators.

The second method relies on the data characteristics of trajectory samples. The disadvantages are strong limitations and poor generalization ability. When the characteristics of trajectory test samples and training samples are significantly different, the clustering quality of the algorithm using training samples to set parameters will inevitably decline.

The third method is adjusted through multiple experiments. There are two disadvantages. One is that multiple experiments significantly increase the user's time cost; not the theoretical optimum

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