Context Enhanced Information Processing Framework for Global Maritime Target Recognition and Surveillance

Global maritime target recognition and surveillance system design is challenged by multi-source heterogenous data, rich contextual information, complex data processing logic, and hidden social/technical constraints for maritime targets. In this paper, we conduct a comprehensive study on the analysis and design of information processing systems for the identification and tracking of global maritime targets, with a focus on key components for cognitive-level, large-scale maritime target recognition and surveillance. Initially, we identify and summarize problems associated with marine and maritime data processing, data governance and data analysis based on large-scale, multi-source heterogeneous data combined with natural, technical, and social contexts. Subsequently, we propose an information processing framework equipped with recent big data techniques to enhance the processing, governance, and analysis capabilities for large-scale and complex interrelated maritime data. Finally, we implement a global ship identification and tracking system that leverages the fusion of social and technical information, which demonstrats the feasibility and effectiveness of our proposed framework.