Navy Hydroacoustic Surveillance System

Abstract

Maintaining maritime security across vast ocean regions is a complex and resource-intensive task, often relying on manual monitoring and conventional radar systems that may fail to detect underwater threats effectively. This paper proposes an AI-Assisted Navy Hydroacoustic Surveillance System that enhances maritime situational awareness by combining underwater acoustic analysis with computer vision techniques. The system processes real-time data from hydrophones and surveillance cameras, applying deep learning models such as CNN and LSTM to detect anomalous underwater sounds including submarine movements, torpedo activity, and unusual vibrations. Simultaneously, computer vision models analyze sea-surface imagery to identify suspicious vessels and objects.
The monitored ocean region is divided into multiple zones, and each zone is assigned a threat score based on normalized outputs from acoustic and visual models. These scores are integrated to generate an overall threat intensity level for each zone. Detected threats are visualized through an interactive dashboard featuring maps, heatmaps, and alert indicators, enabling naval personnel to quickly identify high-risk areas and take appropriate action.
Experimental evaluation on simulated and real-world datasets demonstrates high detection accuracy and efficient real-time processing capabilities.
The proposed system provides a scalable and intelligent framework for multi-modal maritime surveillance, improving threat detection, response time, and decision-making in modern naval operations.

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