在线材料标记环境重建：基于贝叶斯多径归因的低空ISAC
Online Material-Labeled Environment Reconstruction via Bayesian Multipath Attribution for Low-Altitude ISAC
- School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与电子工程学院）
- School of Transportation and Civil Engineering, Nantong University（南通大学交通与土木工程学院）
- College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对低空ISAC环境重建忽视材料信息的问题，提出贝叶斯多径归因在线框架，实现材料标记地图构建，在模拟中达到93.75%立面材料准确率。
AI中文摘要：
低空集成感知与通信（ISAC）的环境重建主要集中在以几何为中心的地图上，忽视了依赖于材料的传播效应。因此，材料标记的重建是实现传播感知地图的关键一步，有助于更符合物理规律的信道预测和无人机（UAV）组网。然而，在室外多建筑场景中，从无线多径观测构建此类地图具有挑战性，因为来自不同立面的多径分量（MPC）混合在一起，路径到立面的归因不确定，且无人机测量在时变观测几何下按顺序到达。为解决这些挑战，我们提出一个统一的在线概率框架，将每个反射立面表示为虚拟锚点（VA），并耦合贝叶斯VA定位、多径归因和材料推断。贝叶斯前端估计立面级几何，并使用镜面-漫反射似然模型计算软MPC到VA的归因概率，从而同时考虑主导镜面路径和漫反射表面交互分量。这些归因概率用于构建归因感知的MPC表示，以VA为中心进行聚合，并由材料推断网络映射为立面级材料证据。所得证据通过在线贝叶斯更新递归融合，产生稳定的材料后验和材料标记的环境地图。在代表性城市街道场景中的射线追踪模拟表明，所提方法显著优于无归因基线，在保留的无人机轨迹上实现了93.75%的最终立面级材料准确率，并保持了基于VA的准确立面定位。
英文摘要：
Environment reconstruction for low-altitude integrated sensing and communications (ISAC) has largely focused on geometry-centric maps, overlooking material-dependent propagation effects. Material-labeled reconstruction is therefore a key step toward propagation-aware mapping, enabling more physically grounded channel prediction and uncrewed aerial vehicle (UAV) networking. However, constructing such maps from wireless multipath observations is challenging in outdoor multi-building scenarios because multipath components (MPCs) from different facades are mixed, path-to-facade attribution is uncertain, and UAV measurements arrive sequentially under time-varying observation geometries. To address these challenges, we propose a unified online probabilistic framework that represents each reflecting facade as a virtual anchor (VA) and couples Bayesian VA localization, multipath attribution, and material inference. The Bayesian front end estimates facade-level geometry and computes soft MPC-to-VA attribution probabilities using a speculardiffuse likelihood model, thereby accounting for both dominant specular paths and diffuse surface-interacted components. These attribution probabilities are used to construct attribution-aware MPC representations, which are aggregated in a VA-centric manner and mapped by a material inference network to facadelevel material evidence. The resulting evidence is recursively fused through an online Bayesian update to produce stable material posteriors and material-labeled environment maps. Ray-tracing simulations in a representative urban street scenario show that the proposed method substantially outperforms a no-attribution baseline, achieves 93.75% final facade-level material accuracy on a held-out UAV trajectory, and maintains accurate VA-based facade localization.
