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Abstract: track-to-track association (T2TA), which aims at unifying track batch numbers and reducing track redundancy, serves as a precondition and foundation for track fusion and situation awareness. The current problems of T2TA come mainly from two sources: track data and association methods. Ubiquitous problems are errors and inconsistent update periods for track data, and suboptimal association results and dependences on prior information and presumed motion models for association methods. Focusing on these two aspects, in this paper, a multiple hypothesis algorithm for multi-sensor track-to-track association with intelligent track score (MH-T2TA) is proposed. A spatial-temporal registration module is designed based on self-attention and a contrastive learning architecture to eliminate errors and unify the distributions of asynchronous tracks. A multiple hypothesis algorithm is combined with deep learning to estimate the association score of a pair of tracks without relying on prior information and presumed motion models, and the optimal association pairs can be obtained. With three kinds of loss functions, tracks coming from the same targets become closer, tracks coming from different targets become more distant, and the estimated track scores are as similar to the real ones as possible. Experiment results demonstrate that the proposed MH-T2TA can associate tracks in complex scenarios and outperform other T2TA methods.