Radar target tracking algorithm based on adaptive DBSCAN

Abstract:

Directly processing the track initiation and tracking of the points recorded in the radar point recorder of the threedimensional air traffic control system will generate a large number of false alarms and a large amount of calculation. When performing target tracking, the number of candidate point sets is huge is the main reason for the large amount of calculation in the target tracking process. Based on the dynamic adaptive DBSCAN clustering algorithm and the classic Kalman filter tracking algorithm, a hybrid dynamic adaptive DBSCAN clustering tracking algorithm is proposed in this paper to reduce the number of candidate point sets. Experiments have found that the number of invalid points is reduced and the track quality is improved Computing time decreases. Through the dynamic adaptive DBSCAN clustering tracking hybrid algorithm, it can quickly track the target detected by cnac radar once and form the target track, which can detect the black flight target in time and reduce the interference to the normal flight of civil aviation aircraft to the minimum.