Multi-sensor data fusion in maritime surveillance: a review on methods and applications

The aim of maritime surveillance is to support maritime security by protecting national and international rights and interests through reliable monitoring systems, thereby increasing situational awareness. As threats continue to grow, ranging from piracy and illegal fishing to environmental hazards like oil spills the critical importance of surveillance to ensure security and support sustainable use of the ocean becomes increasingly evident. However, relying solely on single-sensor data for detection and identification purposes is often inadequate. To increase the likelihood of successful detection and identification of vessels, it is critical that heterogeneous ( i.e ., varying types of) data from several different sensors ( i.e ., radar, optical systems, Automatic Identification System (AIS), and Synthetic Aperture Radar (SAR)) be integrated to eliminate the limitations of individual sensors and create a more comprehensive view of the maritime domain. The focus of this review is to provide a systematic overview of various approaches to multi-sensor fusion with emphasis on artificial intelligence (AI)-based methodologies. The article provides a structured literature review of literature related to the ship detection, recognition, tracking, and anomaly detection using fusion processes and presents and discusses most recent and successful practices, some of the technical challenges that exist today, and potential areas of future research. The evaluation includes both traditional and contemporary AI-based techniques ( i.e ., machine learning and deep learning) as well as the complexities that exist when attempting to handle large volumes of data, process data in real-time, and account for variability in the environment and potential cyber threats. In summary, the ultimate goal of this study is to provide an informative reference, for the purpose of enhancing maritime situational awareness.