用扩散模型先验提升微波链路降雨反演精度
Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
- 将降雨重建视为贝叶斯逆问题，引入扩散模型作为空间先验
- 在合成与真实数据上均优于现有基线方法，尤其在不均匀降水下表现更优
- 无需训练即可使用多种采样方法，适用于实际监测系统部署
商用微波链路（CMLs）提供密集的空间覆盖用于降雨探测，但其测量为路径积分值，难以准确重构地面降雨场。现有方法通常将CML简化为点传感器，并忽略降雨与信号衰减间的路径积分关系，导致在非均匀降水下性能下降。本文将降雨场重建视为贝叶斯逆问题，采用扩散模型（DMs）作为高保真空间先验。实验表明，相比截断高斯过程，扩散模型能更好保持关键降雨统计特性。通过将扩散模型作为先验，可实现无训练的后验采样，支持插件式、序列蒙特卡洛及复制交换等多种方法。在合成与真实数据集上的实验均显示，该方法持续优于现有的基于CML的重建基线。
原文摘要 · Abstract (English)
Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate ground-level reconstruction challenging. Existing methods typically oversimplify CMLs as point sensors and neglect line integration relating rainfall to signal attenuation, resulting in degraded performance under heterogeneous precipitation. In this work, we view rain field reconstruction as a Bayesian inverse problem with Diffusion Models (DMs) as high-fidelity spatial priors. We show that diffusion models better preserve key rainfall statistics compared to censored Gaussian processes. Framing rainfall estimation as a Bayesian inverse problem with a DM prior enables training-free posterior sampling using a broad family of methods, including Plug-and-Play, Sequential Monte Carlo, and Replica Exchange methods. Experiments on synthetic and real-world datasets demonstrate consistent improvements over established CML-based reconstruction baselines.
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