Active Localization of Close-range Adversarial Acoustic Sources for Underwater Data Center Surveillance

Underwater data infrastructures offer natural cooling and enhanced physical security compared to terrestrial facilities, but their storage systems remain susceptible to acoustic injection attacks, where sound-induced mechanical vibrations disrupt critical I/O operations and compromise data availability. This work presents a surveillance framework for localizing and tracking such close-range adversarial acoustic sources targeting offshore infrastructures, particularly underwater data centers (UDCs). We propose a scalable heterogeneous receiver configuration with one facility-mounted hydrophone and one mobile hydrophone carried by a surveillance robot. The resulting problem differs from conventional sound source localization (SSL) due to distributed facility scale, narrowband signaling with phase ambiguity, non-cooperative sources, and mobile receiver state uncertainty. To address these challenges, we formulate a Locus-Conditioned Maximum A-Posteriori (LC-MAP) scheme that generates acoustically informed priors, ensuring a physically plausible initial state for a joint time- and frequency-difference-of-arrival (TDOA-FDOA) filtering. We integrate this into an unscented Kalman filter (UKF) pipeline, along with a multipath-aware measurement model that compensates for surface and bed reflections, and an effective measurement covariance that accounts for mobile receiver uncertainty. Extensive Monte Carlo analyses, fixed-array baseline comparisons, Gazebo-based physics simulations, and field trials demonstrate reliable real-time localization and tracking. The framework achieves sub-meter localization accuracy and over 90% success rates in most scenarios, with convergence times nearly halved compared to baselines. Overall, this study establishes a geometry-aware, real-time approach for acoustic threat localization and advances autonomous surveillance capabilities of underwater infrastructure.