发表机构
NYU Wireless（纽约大学无线通信实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出LOCUS-DT框架，利用数字孪生和学习评分函数处理室内多径定位，泛化性好，在Sionna测试中比高斯类基准更精准捕捉多模态后验结构。
AI 中文摘要
精确的室内定位对于机器人导航和搜救等新兴应用至关重要。尽管经典方法通常聚焦于单点估计，但存在严重遮挡和多径传播的复杂室内环境往往会产生多模态似然面，此时单点估计并不足够。本文提出LOCUS-DT（Localization via Observation-Conditioned Uncertainty Scoring with Digital Twins），该框架将快照定位视为对发射机位置的后验推断。通过利用已知环境的基于射线追踪的数字孪生（DT），LOCUS-DT为候选位置生成合成多径轮廓，并将其与实测信道轮廓进行比较。我们方法的核心是一种新颖的学习评分函数，旨在比较固定数量的主导镜面路径，从而对DT环境模型和物理信道估计中的误差都具有鲁棒性。重要的是，LOCUS-DT在环境集合上进行训练，以确保对未见布局的泛化能力。我们使用基于Sionna的射线追踪后端评估该系统，结果表明，与标准高斯或高斯混合基准相比，LOCUS-DT能更精确地捕捉室内环境固有的尖锐多模态后验结构。
英文摘要
Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue. While classical methods typically focus on single-point estimates, complex indoor environments with heavy blockage and multipath propagation often lead to multimodal likelihood surfaces where a single estimate is insufficient. This paper proposes LOCUS-DT (Localization via Observation-Conditioned Uncertainty Scoring with Digital Twins), a framework that treats snapshot localization as posterior inference over the transmitter location. By leveraging a ray-tracing-based digital twin (DT) of the known environment, LOCUS-DT generates synthetic multipath profiles for candidate locations and compares them against the measured channel profile. Central to our approach is a novel learned scoring function designed to compare a fixed number of dominant specular paths, providing robustness against errors in both the DT environment model and the physical channel estimation. Importantly, LOCUS-DT is trained over an ensemble of environments to ensure generalization to unseen layouts. We evaluate the system using a Sionna-based ray-tracing backend, demonstrating that LOCUS-DT captures the sharp, multimodal posterior structures inherent in indoor settings more accurately than standard Gaussian or Gaussian-mixture benchmarks.
