Multi-Source Heterogeneous Data Fusion Algorithm for Vessel Trajectories in Canal Scenarios

With the globalization of trade, maritime transport is playing an increasingly strategic role in sustaining international commerce. As a result, research into the tracking and fusion of multi-source vessel data in canal environments has become critical for enhancing maritime situational awareness. In the existing research and development, the heterogeneity of and variability in vessel flow data often lead to multiple issues in tracking algorithms, as well as in subsequent trajectory-matching processes. The existing tracking and matching frameworks typically suffer from three major limitations: insufficient capacity to extract fine-grained features from multi-source data; difficulty in balancing global context with local dynamics during multi-scale feature tracking; and an inadequate ability to model long-range temporal dependencies in trajectory matching. To address these challenges, this study proposes the Shape Similarity and Generalized Distance Adjustment (SSGDA) framework, a novel vessel trajectory-matching approach designed to track and associate multi-source heterogeneous vessel data in complex canal environments. The primary contributions of this work are summarized as follows: (1) an enhanced optimization strategy for trajectory fusion based on Enhanced Particle Swarm Optimization (E-PSO) designed for the proposed trajectory-matching framework; (2) the proposal of a trajectory similarity measurement method utilizing a distance-based reward–penalty mechanism, followed by empirical validation using the publicly available FVessel dataset. Comprehensive aggregation and analysis of the experimental results demonstrate that the proposed SSGDA method achieved a matching precision of 96.30%, outperforming all comparative approaches. Additionally, the proposed method reduced the mean-squared error between trajectory points by 97.82 pixel units. These findings further highlight the strong research potential and practical applicability of the proposed framework in real-world canal scenarios.