A Global Optimal Association Algorithm Based on Fuzzy Comprehensive Decision-Making

In this study, a global optimal association algorithm based on fuzzy comprehensive decision-making is proposed to enhance the accuracy and computational efficiency of associating radar and Automatic Identification System (AIS) track data. The algorithm combines spatial partitioning to reduce computational complexity, and utilizes error calibration and global optimal methods to enhance association accuracy. The experimental results show that the proposed algorithm achieves higher association accuracy and shorter running time compared to traditional track association algorithms. This improvement is particularly notable when handling a large number of tracks.