An Improved Algorithm for Flight Maneuver Recognition and Evaluation Based on Support Vector Machines

Xiaokang Li, Tianyi Zhu, Zimu Bian, Zhuxin Yu, Xiaoguang Gao, Kaifang Wan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The advancement of defense capabilities relies heavily on improving air combat proficiency. Effective pilot training plays a pivotal role in achieving this goal. Simulated flight training is a critical method for training pilots, and leveraging intelligent scoring algorithms can significantly enhance pilot proficiency. In this study, we propose an enhanced SVM algorithm that incorporates PCA for dimensionality reduction. By combining pilot training-related data from flight simulators with advanced machine learning techniques, we aim to develop an intelligent digital instructor system. This system provides real-time, objective, and quantitative assessments, along with detailed diagnostic feedback to pilot trainees. Furthermore, the algorithm's potential extends beyond civilian pilot training to autonomous air combat scenarios involving UCAVs in the future.

Original languageEnglish
Title of host publication2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350376739
DOIs
StatePublished - 2024
Event2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024 - Doha, Qatar
Duration: 8 Nov 202412 Nov 2024

Publication series

Name2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

Conference

Conference2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024
Country/TerritoryQatar
CityDoha
Period8/11/2412/11/24

Keywords

  • pilot training
  • Principal Component Analysis
  • Support Vector Machine

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