PropSplat: 通过3D高斯传播散射实现无地图的射频场重建
PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting
AI总结：
PropSplat利用3D各向异性高斯体重建射频场，无需地理数据或外部信息，通过端到端优化实现精准传播建模，优于现有方法。
摘要：
构建特定地点的传播模型通常需要射线追踪详细3D地图或密集测量活动。这两种方法成本高且难以快速部署，尤其是当地理数据不可用或过时时。我们提出了PropSplat，一种无地图的传播建模方法，利用3D各向异性高斯体重建射频（RF）场。每个高斯体编码一个标量路径损耗偏移量，相对于显式基准路径损耗模型，具有可学习的路径损耗指数。高斯体初始化于观测到的发射机-接收机路径上，并通过端到端优化学习传播环境，无需外部信息如楼层计划、地形数据库或障碍物数据。我们评估了PropSplat与无线辐射场方法NeRF$^2$、GSRF和WRF-GS+在两个真实世界数据集上的性能。在覆盖多个地形区域的大型户外道路测试中，PropSplat在300米间隔的训练测量下达到5.38 dB的RMSE，优于WRF-GS+（5.87 dB）、GSRF（7.46 dB）和NeRF$^2$（14.76 dB）。在室内蓝牙低能耗测量中，PropSplat达到0.19米的平均定位误差，比NeRF$^2$（1.84米）高一个数量级，同时实现几乎相同的接收到的信号强度预测精度。这些结果表明，从稀疏的射频原生测量中可以实现准确的特定地点传播重建。地理数据作为可扩展射频环境建模的前提需求被减少。
英文摘要：
Building a site-specific propagation model typically requires either ray-tracing over detailed 3D maps or dense measurement campaigns. Both approaches are expensive and often infeasible for rapid deployments where geographic data is unavailable or outdated. We present PropSplat, a map-free propagation modeling method that reconstructs radio frequency (RF) fields using 3D anisotropic Gaussian primitives. Each Gaussian encodes a scalar path loss offset relative to an explicit baseline path loss model with a learnable path loss exponent. Gaussians are initialized along observed transmitter--receiver paths and optimized end-to-end to learn the propagation environment without external information like floor plans, terrain databases, or clutter data. We evaluate PropSplat against wireless radiance field methods NeRF$^2$, GSRF, and WRF-GS+ on two real-world datasets. On large-scale outdoor drive-tests spanning multiple topographical regions at six sub-6 GHz frequencies, PropSplat achieves 5.38 dB RMSE when training measurements are spaced 300m apart and outperforms WRF-GS+ (5.87 dB), GSRF (7.46 dB), and NeRF$^2$ (14.76 dB). On indoor Bluetooth Low Energy measurements, PropSplat achieves 0.19m mean localization error, an order of magnitude better than NeRF$^2$ (1.84m), while achieving near-identical received signal strength prediction accuracy. These results show that accurate site-specific propagation reconstruction is achievable from sparse RF-native measurements. The need for geographic data as a prerequisite for scalable RF environment modeling is reduced.
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