Autonomous Early-Warning Systems for Maritime Piracy Threat Detection Using AI-Based Sensor Fusion

Maritime piracy continues to pose a serious threat to commercial shipping, especially in regions where surveillance coverage is limited and response time is critical. Conventional anti-piracy measures rely primarily on human watchkeeping and isolated monitoring systems, which may be insufficient for early threat recognition. The rapid development of autonomous systems and artificial intelligence enables new approaches to maritime situational awareness based on continuous, automated analysis of multisource data.

Here, we present a conceptual design of an autonomous early-warning system for maritime piracy threat detection based on AI-driven sensor fusion. The proposed framework integrates heterogeneous data from Automatic Identification System (AIS), marine radar, and electro-optical/infrared (EO/IR) sensors into a unified operational picture. A data fusion layer is combined with machine learning algorithms for anomaly detection and vessel behavior classification, enabling identification of potentially hostile units and atypical navigation patterns. The system architecture includes an onboard edge-computing unit responsible for real-time data processing and automatic generation of threat alerts for the ship’s bridge team.

The article discusses key design requirements, data processing pipelines, decision-making logic, and integration with existing shipboard control and communication systems. Particular attention is paid to autonomy, reliability, and reducing false-alarm rates in complex maritime environments. The proposed concept demonstrates how AI-based sensor fusion can enhance early threat awareness and support crew decision-making in piracy-prone waters.

The presented framework provides a foundation for future development and practical implementation of autonomous maritime security systems on commercial vessels.