基于核主成分分析的空域复杂度无监督评估

doi:10.7527/S1000-3893.2019.22969

Abstract

Abstract: Airspace complexity evaluation is a key means to measure the airspace operational situation and the controller workload, providing the basis for the operation optimization. Its accurate evaluation is a challenging problem in the aviation domain due to numerous influencing factors, the complex correlations between factors, and the high difficulty of collecting labelled samples. This paper proposes an unsupervised evaluation method for airspace complexity. Firstly, the kernel principal component analysis is utilized to mine the nonlinear correlations in different sample dimensions, and extract several principal components in which the airspace complexity information is maximized. Furthermore, the principal component clustering which can be customized according to user requirements is designed. The proposed method achieves accurate complexity evaluation capacity under the unsupervised condition, providing effective technical support for air traffic management like airspace configuration and traffic management.

Key words: airspace complexity, airspace operation situation, unsupervised learning, kernel principal component analysis, dimension reduction, clustering

CLC Number:

V355.1

ZHANG Zhuxi, ZHU Xi, ZHU Shaochuan, ZHANG Mingyuan, DU Wenbo. Unsupervised evaluation of airspace complexity based on kernel principal component analysis[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019, 40(8): 322969-322969.

References 23

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Unsupervised evaluation of airspace complexity based on kernel principal component analysis

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