AI 中文总结
针对漂移分布未知时被动TDOA定位的传感器部署问题，推导漂移误差下的GDOP，构建极小极大优化问题，提出AUPSO算法，为工程场景提供可靠的离线部署规划框架。
AI 中文摘要
本文研究当传感器位置受风等因素导致的漂移误差影响时，如何离线部署传感器以在整个感兴趣区域（ROI）提供鲁棒的被动TDOA定位精度。由于实际中仅能从历史传感器遥测数据或风场统计中估计传感器漂移误差的一阶和二阶统计量，我们利用这些量首先推导了漂移误差下的广义几何精度因子（$\boldsymbol{\rm GDOP_D}$），其扩展了传统几何精度因子（$\boldsymbol{\rm GDOP_T}$）。此外，我们推导了与$\boldsymbol{\rm GDOP_D}$和$\boldsymbol{\rm GDOP_T}$相关的理论结果，揭示漂移误差不仅会增大GDOP的值，还会重塑其分布，从而降低定位性能。随后，我们构建了基于$\boldsymbol{\rm GDOP_D}$的极小极大部署优化问题。最后，我们提出了自适应单向粒子群优化器（AUPSO）来解决这一具有挑战性的问题，该方法缓解了传统PSO的早熟收敛和振荡行为。大量仿真验证了所提方法的有效性，本研究为无法获取准确漂移误差概率密度函数的实际工程场景提供了可靠的离线传感器部署规划框架。
英文摘要
This paper investigates how to deploy sensors offline to provide robust passive TDOA localization accuracy across the entire region of interest (ROI) when their positions are subject to drift errors caused by factors such as wind. Since in practice only the 1st and 2nd order statistics of sensor drift errors can be estimated from historical sensor telemetry data or wind field statistics, by using them we first derive a generalized geometric dilution of precision under drift errors ($\mathrm{GDOP_{D}}$), which extends the traditional GDOP ($\mathrm{GDOP_{T}}$). Furthermore, we derive theoretical results related to $\mathrm{GDOP_{D}}$ and $\mathrm{GDOP_{T}}$, revealing that drift errors not only enlarge the value of GDOP but also reshape its distribution, thereby degrading localization performance. Then, we construct a $\operatorname{{GDOP}_{D}}$-based min-max deployment optimization problem. {Finally, we propose an adaptive unidirectional particle swarm optimizer (AUPSO) to solve this challenging problem. The proposed method alleviates the premature convergence and the oscillatory behavior of the traditional PSO. Extensive simulations demonstrate the effectiveness of the proposed method. This research provides a reliable offline sensor deployment planning framework for practical engineering scenarios, when the accurate drift error probability density function is not available.}
