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arXiv 2510.20122eess.SP
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水下数据中心监控中近距离对抗声源的主动定位

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

University of Florida(佛罗里达大学)

Adnan Abdullah, David Blow, Sara Rampazzi, Md Jahidul Islam

AI总结:

针对水下数据中心面临的声学注入攻击威胁,提出一种基于异构接收器配置和Locus-Conditioned最大后验估计的主动定位与跟踪框架,实现亚米级定位精度和超过90%的成功率。

摘要:

与陆地设施相比,水下数据基础设施提供自然冷却和增强的物理安全性,但其存储系统仍然容易受到声学注入攻击,其中声音引起的机械振动会破坏关键的I/O操作并危及数据可用性。本文提出了一种监控框架,用于定位和跟踪针对近海基础设施(特别是水下数据中心(UDC))的此类近距离对抗声源。我们提出了一种可扩展的异构接收器配置,包括一个安装在设施上的水听器和一个由监控机器人携带的移动水听器。由于分布式设施规模、具有相位模糊性的窄带信号、非合作源以及移动接收器状态的不确定性,由此产生的问题与传统的声源定位(SSL)不同。为了解决这些挑战,我们制定了一种Locus-Conditioned最大后验(LC-MAP)方案,该方案生成声学信息先验,为联合到达时间差和到达频率差(TDOA-FDOA)滤波确保物理上合理的初始状态。我们将其集成到无迹卡尔曼滤波器(UKF)流程中,并配有多路径感知测量模型(补偿表面和底部反射)以及考虑移动接收器不确定性的有效测量协方差。广泛的蒙特卡洛分析、固定阵列基线比较、基于Gazebo的物理仿真和现场试验证明了可靠的实时定位和跟踪。该框架在大多数场景中实现了亚米级定位精度和超过90%的成功率,收敛时间比基线几乎缩短了一半。总体而言,本研究建立了一种几何感知的实时声学威胁定位方法,并推进了水下基础设施的自主监控能力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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