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 include errors and inconsistent update periods in track data, as well as suboptimal association results and dependencies on prior information and assumed motion models for association methods. Focusing on these two aspects, we propose a multiple-hypothesis algorithm for multi-sensor T2TA with an intelligent track score (MH-T2TA). 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 or assumed 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 very similar to the real ones. Experimental results demonstrate that the proposed MH-T2TA can associate tracks in complex scenarios and outperform other T2TA methods.

Key words: Track-to-track association; Multiple-hypothesis algorithm; Track score; Neural networks

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