Architecture for ROVs detection and positioning based on hydrophone arrays

This paper presents an integrated architecture for detecting and positioning Remotely Operated Vehicles (ROVs) using strategically placed hydrophone arrays. We introduce two types of hydrophones with distinct sensitivity and bandwidth characteristics, deployed in both controlled (swimming pool) and semi-controlled (harbor) environments. The proposed system leverages frequency-domain (Discrete Fourier Transform, DFT) and time-frequency (Short-Time Fourier Transform, STFT) analyses to highlight key acoustic signatures, such as characteristic motor and propeller frequencies, enabling reliable ROV detection under variable noise conditions. We then incorporate propagation modeling and multi-hydrophone geometry to estimate the vehicle’s position, drawing on both amplitude- and time-based techniques. The experimental results demonstrate robust performance in different aquatic settings, confirming the utility of high-sensitivity hydrophones and carefully selected signal processing parameters for enhanced detection and localization. Our findings emphasize how combining versatile hardware, advanced acoustic processing, and adaptive array configurations can address the challenges posed by complex underwater environments, laying the groundwork for broader applications such as harbor security, environmental monitoring, and maritime traffic management.