Improved PSO-GA-based LSSVM flight conflict detection model

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.