Summary and outlook of 4D track prediction methods

Abstract

Four-dimensional track prediction is the precise control of the time dimension of threedimensional space at each stage of the flight. Accurate prediction of the aircraft trajectory is a prerequisite for the automation of air traffic control. This article reviews 4D track prediction technology, that is, particle motion-based prediction method, hybrid estimation-based prediction method and machine learning-based prediction method. At the end, combined with aircraft track data, the method of data mining is used to extract the flight profile and describe the applicable scope of each method. The contents of this paper can be used for reference in the research direction of 4D track prediction technology.

Center trajectory extraction algorithm based on multidimensional hierarchical clustering

“…The central trajectory is the most representative one with the highest density in the traffic flow. The central trajectory can be applied to aircraft trajectory prediction [4], abnormal trajectory recognition [5], practical analysis of departure procedures [6], and airport noise control [7]. The main technical idea of central trajectory extraction is to measure the similarity between tracks and cluster large-scale track data into traffic flows with large intra-class similarity and small inter-class similarity [8][9][10].…”