AI 中文总结
针对水下物联网网络 TDOA 定位难题，提出 ISAC-TM-MAE 框架，通过联合通信与定位，依据多种约束选节点对，用 D 最优准则最大化 FIM，经 MAE 阶段细化估计，提升了定位精度、资源效率、可扩展性及鲁棒性。
AI 中文摘要
水下物联网（IoUT）中的可靠定位受到声学带宽有限、传播延迟长、多径、多普勒频移和能量受限节点的阻碍。本文提出了一种用于 IoUT 网络中到达时间差（TDOA）定位的集成传感与通信（ISAC）启用的拓扑管理和多阶段自适应估计（ISAC-TM-MAE）框架。在该框架中，异构水下传感器、海底锚、自主水下航行器（AUV）和水面浮标利用声学 ISAC 数据包进行联合通信和定位。将 IoUT 网络建模为一个图，在其中根据声学带宽、计算、能量和链路可靠性约束选择信息丰富的节点对。一个 D 最优准则在感兴趣的水下区域最大化 Fisher 信息矩阵（FIM），而 MAE 阶段在声学噪声和信道不确定性下细化源估计。数值结果表明，与集中式、分布式和基准 TDOA 定位方案相比，所提出的 ISAC-TM-MAE 提高了定位精度、资源效率、可扩展性和鲁棒性。
英文摘要
Reliable localization in the Internet of Underwater Things (IoUT) is hindered by limited acoustic bandwidth, long propagation delays, multipath, Doppler shifts, and energy-constrained nodes. This letter proposes an integrated sensing and communication (ISAC)-enabled topology management and multi-stage adaptive estimation (ISAC-TM-MAE) framework for time-difference-of-arrival (TDOA) localization in IoUT networks. In the proposed framework, heterogeneous underwater sensors, seabed anchors, autonomous underwater vehicles (AUVs), and surface buoys exploit acoustic ISAC packets for joint communication and localization. The IoUT network is modeled as a graph, in which informative node pairs are selected subject to acoustic bandwidth, computation, energy, and link-reliability constraints. A D-optimal criterion maximizes the Fisher information matrix (FIM) over the underwater region of interest, while the MAE stage refines the source estimate under acoustic noise and channel uncertainty. Numerical results show that the proposed ISAC-TM-MAE improves localization accuracy, resource efficiency, scalability, and robustness compared with centralized, distributed, and benchmark TDOA localization schemes.
Comments6 Pages, 2 Figures with 6 Subfigures in Fig. 2, Submitted to IEEE for Possible Publication
