Efficient Marine Track-to-Track Association Method for Multi-Source Sensors

Constructing reliable trajectories for ship targets in busy waterways involves associating tracks from various sensor sources. This poses challenges due to the incoherence in sensor data precision and acquisition frequency, the high density of targets, and the intertwined correlation scenarios involving trajectory interruptions and multi-source (also known as multi-sensor) observations. Utilizing the intuitive idea that a ship's motion pattern is influenced by its geographic location, we integrate geographic and dynamic information to enhance trajectory accuracy in dense scenes. The proposed approach employs self-supervised contrastive learning to extract relevant features, accompanied by a dual-level augmentation strategy tailored to two association scenarios. Additionally, a specialized feature enhancement module is introduced to enable a comprehensive representation of trajectories. When tested on a large dataset and compared with established methods, our method demonstrates superior capabilities in extracting trajectory features and excels in a variety of association contexts.