AI 中文总结
针对水下物联网网络中无源/半无源标签定位难题，提出无时钟共发射机TDoA框架，通过接收机端差分消除相关延迟，用加权非线性最小二乘估计器结合多种因素，仿真显示该方法能降低误差、提高成功概率。
AI 中文摘要
声学反向散射可实现低功耗的水下物联网（IoUT）节点，但在弱回波以及海面/海底多径情况下，对无源/半无源标签进行定位具有挑战性。本文提出了一种无时钟的共发射机到达时间差（TDoA）框架，由一个锚点询问标签，同步接收机测量反向散射到达时间。接收机端差分消除了未知的询问时间、前向锚点到标签的延迟以及标签切换延迟。加权非线性最小二乘估计器结合了信噪比（SNR）、定时方差和均方根（RMS）延迟扩展。与仅基于接收信号强度指示（RSSI）、未加权、SNR加权和鲁棒TDoA基线的仿真表明，在20 dB SNR时，均方根误差（RMSE）从4.71 m降至4.13 m，5 m成功概率从0.793提高到0.835。
英文摘要
Acoustic backscatter enables low-power Internet of Underwater Things (IoUT) nodes, but localizing passive/semi-passive tags is difficult under weak returns and surface/seabed multipath. This letter proposes a clock-free common-transmitter time-difference-of-arrival (TDoA) framework, in which one anchor interrogates the tag and synchronized receivers measure the backscattered arrivals. Receiver-side differencing cancels the unknown interrogation time, forward anchor-to-tag delay, and tag switching delay. A weighted nonlinear least-squares estimator combines signal-to-noise ratio (SNR), timing variance, and root-mean-square (RMS) delay spread. Simulations against received signal strength indicator (RSSI)-only, unweighted, SNR-weighted, and robust TDoA baselines show that at $20$ dB SNR, the root-mean-square error (RMSE) decreases from $4.71$ m to $4.13$ m, and the $5$ m success probability improves from $0.793$ to $0.835$.
Comments6 Pages, 3 Figures, Submitted to IEEE for Possible Publications
