Title: RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimation Show Chinese title

Title: 无线电扩散定位：增强的散射认知用于稀疏无线电图估计的非视距定位

Abstract: Accurate localization of non-cooperative signal sources in non-line-of-sight (NLoS) environments remains a critical challenge with a wide range of applications, including autonomous navigation, industrial automation, and emergency response. In such settings, traditional positioning techniques relying on line-of-sight (LoS) or cooperative signaling fail due to severe multipath propagation and unknown transmit power. This paper proposes a novel generative inference framework for NLoS localization based on conditional diffusion models. By leveraging the physical insight that diffracted electromagnetic energy concentrates near building edges, we develop a sampling strategy that collects sparse received signal strength (RSS) measurements at the geometric vertices of obstacles--locations that maximize Fisher information and mutual information with respect to the unknown source. To overcome the lack of known transmission power, we normalize all sampled RSS values relative to the maximum observed intensity, enabling the construction of a power-invariant radio map (RM). A conditional diffusion model is trained to reconstruct the full RM based on environmental layout and sparse RSS observations. Localization is then achieved by identifying the brightest point on the generated RM. Moreover, the proposed framework is compatible with existing RSS-based localization algorithms, enabling a dual-driven paradigm that fuses physical knowledge and data-driven inference for improved accuracy. Extensive theoretical analysis and empirical validation demonstrate that our approach achieves high localization accuracy with significantly reduced sampling cost, offering a scalable and physically grounded solution for non-cooperative NLoS emitter localization. Show Chinese abstract

Abstract: 非视距（NLoS）环境中非合作信号源的精确定位仍然是一个具有广泛应用的关键挑战，包括自主导航、工业自动化和应急响应。在这些情况下，依赖于视距（LoS）或合作信号的传统定位技术由于严重的多径传播和未知的发射功率而失效。本文提出了一种基于条件扩散模型的新型生成推理框架，用于NLoS定位。通过利用衍射电磁能量集中在建筑物边缘的物理洞察，我们开发了一种采样策略，在障碍物的几何顶点处收集稀疏的接收信号强度（RSS）测量值——这些位置相对于未知源最大化了费舍尔信息和互信息。为克服已知发射功率的缺乏，我们将所有采样的RSS值相对于最大观测强度进行归一化，从而构建了一个与功率无关的无线电图（RM）。训练一个条件扩散模型，根据环境布局和稀疏的RSS观测重建完整的RM。然后通过识别生成的RM上的最亮点来实现定位。此外，所提出的框架与现有的基于RSS的定位算法兼容，能够实现融合物理知识和数据驱动推理的双驱范式，以提高准确性。广泛的理论分析和实证验证表明，我们的方法在显著降低采样成本的情况下实现了高定位精度，为非合作NLoS发射器定位提供了一个可扩展且物理基础的解决方案。