High-fidelity spectrum cartography is important for spectrum monitoring and wireless situational awareness, especially in satellite-based wide-area sensing scenarios where measurements are sparse, noisy, and often low-bit quantized. In such settings, two coupled challenges arise: accurate reconstruction from severely incomplete measurements and efficient allocation of additional sensing resources under a limited sensing budget. Existing methods usually address these problems separately, and, for reconstruction, they often rely on priors that are insufficiently expressive under sparse and quantized measurements. This paper proposes Generative Spectrum Cartography (GSC), a diffusion-based posterior inference framework for spectrum cartography with uncertainty-aware active sensing. Specifically, spectrum map recovery is formulated as a Bayesian inverse problem under a learned diffusion model prior, and closed-form posterior mean updates are derived for both linear and quantized measurement models. By embedding these updates into the reverse diffusion process, GSC enables gradient-free and measurement-consistent posterior sampling without relying on computationally costly likelihood-gradient guidance. The resulting posterior samples are further used to estimate spatial uncertainty and to guide diversity-aware selection of additional measurement locations for active sensing. Experiments on simulated electromagnetic maps and a high-fidelity simulated satellite monitoring scenario show that GSC achieves higher PSNR, lower LPIPS, and more efficient sensing than representative baseline methods under sparse, noisy, and low-bit quantized measurements.

翻译：高保真频谱地图绘制对于频谱监测与无线态势感知至关重要，特别是在卫星广域感知场景中，测量数据通常呈现稀疏、含噪且低位量化特性。在此类场景中面临两大耦合挑战：从严重不完整测量中精确重建，以及在有限感知预算约束下高效分配额外感知资源。现有方法通常分别解决这些问题，且在重建任务中常依赖对稀疏与量化测量数据表达能力不足的先验信息。本文提出生成式频谱地图绘制（GSC），这是一种基于扩散模型的后验推断框架，支持具有不确定性感知的主动感知。具体而言，频谱图恢复被建模为基于学习扩散模型先验的贝叶斯逆问题，并针对线性与量化测量模型推导出闭合形式的后验均值更新公式。通过将这些更新嵌入逆向扩散过程，GSC无需依赖计算成本高昂的似然梯度引导即可实现无梯度且与测量一致的后验采样。进一步利用生成的后验样本估计空间不确定性，并指导主动感知中新增测量位置的多样性感知选择。在模拟电磁地图与高保真卫星监测场景上的实验表明，在稀疏、含噪及低位量化测量条件下，GSC相比代表性基线方法实现了更高PSNR、更低LPIPS及更高效感知性能。