Improved PSO-GA-based LSSVM flight conflict detection model

Qiting Liu; Qi Wang; Yulin Cao; Jinyue Wang

doi:10.1117/12.3000794

9 August 2023 Improved PSO-GA-based LSSVM flight conflict detection model

Qiting Liu, Qi Wang, Yulin Cao, Jinyue Wang

Proceedings Volume 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023); 127820K (2023) https://doi.org/10.1117/12.3000794
Event: Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 2023, Kuala Lumpur, Malaysia

Abstract

With the rapid development of civil aviation industry, the air traffic flow is increasing, which brings a large load to air traffic control, airports and other units, the safety of flight activities has become a research hotspot, flight conflict detection is a necessary link to ensure the safety of flight activities, the increase in air traffic flow requires its more accurate, efficient and stable operation. Based on the least squares support vector machine (LSSVM) in machine learning, this study uses the information provided by ADS-B, such as heading, position and altitude, combined with the regulations and conflict protection zones in actual operation, to classify the occurrence and severity of flight conflicts under the same moment, i.e., to perform multiple classifications, and uses a hybrid optimization algorithm of genetic + particle swarm to optimize this support vector machine model, and proposes A very efficient and accurate real-time flight conflict detection model is proposed. Finally, simulation analysis shows that the support vector machine is faster and more accurate than the traditional SVM, and has excellent conflict detection capability, and by differentiating the classified conflict levels and performing supervised learning, it can provide accurate warnings for upcoming flight conflicts, which can draw early attention of ATCs and provide a basis for the next flight conflict resolution. Eventually, the conflict detection model is expected to be compatible to airborne and ground surveillance equipment, which can significantly improve the safety of flight activities and has a broad application prospect and important research value.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiting Liu, Qi Wang, Yulin Cao, and Jinyue Wang "Improved PSO-GA-based LSSVM flight conflict detection model", Proc. SPIE 12782, Third International Conference on Image Processing and Intelligent Control (IPIC 2023), 127820K (9 August 2023); https://doi.org/10.1117/12.3000794

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Evolutionary algorithms

Instrument modeling

3D modeling

Particles

Mathematical optimization

Particle swarm optimization

Show All Keywords

Keywords/Phrases

Search In:

Publication Years