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Citation: WANG Ershen, SONG Yuanshang, TONG Gang, et al. Improved conflict detection model of low-altitude flight based on support vector machine[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(1): 8-14. doi: 10.13700/j.bh.1001-5965.2020.0533(in Chinese)
shu

Improved conflict detection model of low-altitude flight based on support vector machine

doi: 10.13700/j.bh.1001-5965.2020.0533
  • 1.

    College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China

  • 2.

    Liaoning General Aviation Academy, Shenyang Aerospace University, Shenyang 110136, China

  • 3.

    School of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China

Funds:

National Natural Science Foundation of China 62173237

National Natural Science Foundation of China 61571309

Key R & D Projects of Liaoning Province 2020JH2/10100045

Natural Science Foundation of Liaoning Province 2019-MS-251

Talent Project of Revitalization Liaoning XLYC1907022

High-Level Innovation Talent Project of Shenyang RC190030

More Information
  • Corresponding author: WANG Ershen, E-mail: wanges_2016@126.com
  • Received Date: 21 Sep 2020
  • Accepted Date: 11 Dec 2020
  • Publish Date: 20 Jan 2022
    Abstract

  • In order to ensure the flight safety of general aviation aircraft in low-altitude airspace, an improved model of flight conflict detection based on support vector machine (SVM) is proposed. First, according to the physical form and flight status of the aircraft, a protection zone suitable for the general aircraft is established. Then, the improved ID3 decision tree algorithm is used to reduce the search space to a local method to select aircraft with potential flight conflicts, and choose the appropriate training set by random forest (RF) method. Finally, the tanh function is used to optimize the probability mapping of the easily saturated sigmoid function to the SVM classification results. Through simulation verification and contrastive analysis, the results show that the DBSACN algorithm based on density clustering is used to remove outliers, and the data generated by false alarms and missing alarms are removed as the training set to optimize the SVM classifier. Therefore, using improved flight conflict detection model, the false alarms and missing alarms are reduced by 0.6% and 1.6% respectively, and the execution efficiency of the algorithm is improved. The model has better anti-interference ability and stability.

     

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