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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2019, Vol. 40 ›› Issue (8): 322969-322969.doi: 10.7527/S1000-3893.2019.22969

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Unsupervised evaluation of airspace complexity based on kernel principal component analysis

ZHANG Zhuxi1,2, ZHU Xi1,3, ZHU Shaochuan1,4, ZHANG Mingyuan1,5, DU Wenbo1,5   

  1. 1. National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation, Beihang University, Beijing 100083, China;
    2. Unit 32751 Force of PLA, China;
    3. Research Institute of Frontier Science, Beihang University, Beijing 100083, China;
    4. School of Transportation Science and Engineering, Beihang University, Beijing 100083, China;
    5. School of Electronic and Information Engineering, Beihang University, Beijing 100083, China
  • Received:2019-02-26 Revised:2019-03-06 Online:2019-08-15 Published:2019-08-26
  • Supported by:
    National Science Fund for Distinguished Young Scholars (61425014); National Science Foundation for Excellent Youth (61722102); National Natural Science Foundation of China (61671031)

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: