Track Segment Association Method Based on Bidirectional Track Prediction and Fuzzy Analysis

Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, the phenomenon of track breakages occur in the process of aircraft track data processing. It leads to the change in target label attributes. In order to make the track segment association effect better, we studied several existing time series prediction methods, and proposed a track segment association method based on bidirectional Holt-Winters prediction and fuzzy analysis. This algorithm bidirectionally predicts and extrapolates track segments by the Holt-Winters method, and then uses the fuzzy track segment association algorithm to perform segment association and secondary association. The simulation results of this method show that the track segment association method based on Holt-Winters prediction and fuzzy analysis can effectively solve the track association problem where the target label attributes change before and after track breakage, demonstrating better association ability and robustness. Compared with the fuzzy association method without adding track prediction, our method generally improves the association accuracy by 35%.